Image, video, voice and training
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Owners also use the unlocked CMP 170HX for image and video diffusion (mostly in ComfyUI), for voice and audio models, and for some training. An unlocked 8 GB card has 64 GB, enough to hold a full video model such as MiniMax H3 at INT8 on one card, so the usual layout is one render job per card; diffusion does not split well across cards. The card has no native FP8 and modest FP32 throughput, so INT8 ConvRot and BF16 paths do best. Training is the contested part: single-card LoRA work runs, multi-card training is limited by the PCIe link. Numbers are one owner's report unless the entry says Proven. LLM serving is on Recipes.
Single-card render times at a glance
Times are for the whole clip unless the row says per step. Models, quants and workflows differ from row to row; the entries further down give the detail and the status of each number.
| Workload | Card | Settings | Time | Credit |
|---|---|---|---|---|
| MiniMax H3, default ComfyUI workflow | 64 GB card | 864x480 | 228 s from cold, 126 s with the models loaded | Source: @ju5t5omedude, 2026-08-04 |
| MiniMax H3, default ComfyUI workflow | 64 GB card | about 720p (0.9 MP), about 45 GB VRAM | 429 s, about 20 s/it | Source: @ju5t5omedude, 2026-08-04 Source: @auxiliary_baka, 2026-08-04 |
| MiniMax H3 | 1x 170HX | 1280x720, 10 s clips | about 53 s per step | Source: @bayley_w, 2026-08-09 |
| MiniMax H3, INT8 ConvRot, ComfyUI | 1x unlocked card, fully in VRAM | 960x544, 5 s, 20 steps | about 200 s | Source: @smit1237, 2026-08-19 |
| MiniMax H3, INT8 | 1x 170HX | 6 s at 864x480; 8 s at 960x544; 8 s HD | 220 s; about 5 min; about 15 min | Source: @smit1237, 2026-09-06 |
| MiniMax H3, INT8 ConvRot, ComfyUI (patched) | 1x 10 GB card at 40 GB, x1 slot | 16:9 at 0.5 MP, draft or moderate step counts | 5-10 min | Source: @lycorascence, 2026-08-26 Source: @dino_38975, 2026-08-29 |
| MiniMax H3 I2V | 1x 64 GB card | 30 s at 640x960, 24 fps (then RIFE to 48 fps) | about 13-14 min | Source: @phoenixone7, 2026-09-13 |
| MiniMax H3 | 1x 170HX | 8 s at 720p, no turbo LoRA | about 15 min | Source: @smit1237, 2026-09-13 |
| MiniMax H3 | 1x 170HX | 10 s at about 1024x7xx | about 3 min | Source: @sojun80, 2026-09-13 |
| MiniMax H3 | 1x 8 GB card (unlocked) | 8 s clip | about 10-15 min | Source: @domrockt2, 2026-10-07 |
| LTX 2.3 | 1x 170HX | about 24 s, upscaled to 896x1152, interpolated to 48 fps | about 350 s; 6-8 min for the whole workflow | Source: @phoenixone7, 2026-09-01 |
| LTX 2.3, no optimization | 1x 170HX | about a 30 s clip | about 2 min | Source: @ju5t5omedude, 2026-07-24 |
| Z-Image Turbo, ComfyUI | 1x 170HX | 1024x1024, 8 steps | under 4 s (RTX 3090: about 7-8 s) | Source: @cmdrwaka324, 2026-08-26 |
Setting up ComfyUI for the card
INT8 ConvRot needs a CUDA 13 driver and a CUDA 13 toolkit
The CMP 170HX gets CUDA 13 and with it INT8 ConvRot (int8convrot); a V100 (Volta) is locked out because it has no CUDA 13 support. You need a CUDA 13 driver and an inference environment built on at least the CUDA 13 toolkit; ComfyUI's built-in nodes can then run it. Lycorascence uses it for image and video generation, not for LLMs. Petri Krohn linked an article on INT8 ConvRot and pointed to the related Hadamard-rotation methods for LLMs (QuaRot, SpinQuant); the LLM side is on Quantization on the 170HX.
Reported
Source: @lycorascence, 2026-08-20
Source: @petrikrohn_26497, 2026-08-21
ComfyUI speedups: the CUDA 13 build, ComfyUI-INT8-Fast, SageAttention plus Triton
For slow stock ComfyUI on the 170HX, Baka recommended on 2026-08-04:
- Install the CUDA 13 build of ComfyUI.
- Add the ComfyUI-INT8-Fast custom node (made for MiniMax H3).
- Install SageAttention plus Triton (Triton only so that Sage runs without errors), worth 5% to 20%.
Keep the int8 convrot H3 weights fully in VRAM; an NVFP4 text encoder is enough, and any offload to system RAM hurts performance badly. The advice came from a 3060 owner and was not measured on a 170HX in that thread. Phoenix17 later found ComfyUI-INT8-Fast gave no INT8 speedup on the card (next entry).
Reported
Source: repo github.com/BobJohnson24/ComfyUI-INT8-Fast by BobJohnson24 on GitHub, posted by @auxiliary_baka, 2026-08-04
Source: @roger_dammit, 2026-08-04
INT8 vs FP8 in ComfyUI: FP8 dequantizes to BF16, INT8 ConvRot wins once torch is on cu130
Phoenix17 (2026-09-01) found ComfyUI-INT8-Fast gave no INT8 speedup on the CMP (issue 71), while FP8 weights, which dequantize to BF16 that these cards handle well, gave about 2.5x. After he upgraded torch to cu130 to enable the CUTLASS fused backend, INT8 ConvRot became about 15 to 22% faster than the FP8 (BF16 dequant) path, tested on LTX 2.3 video generation. roger_dammit, on the H3 video model under CUDA 13, found FP8 slightly slower than ConvRot INT8 and saw no gain from FP8 on the diffusion model, the VAE or the VLM. Why the card has no native FP8: Compute, data types and missing features.
Reported
Source: @phoenixone7, 2026-09-01
Source: @roger_dammit, 2026-09-02
NVFP4 diffusion quants are software-emulated and slow on the card
meatsus (2026-08-26) found the NVFP4 quant of MiniMax H3 very slow on the 170HX because FP4 is software emulated on this card. He also reports a ComfyUI bug that prevents full INT8 performance on the 170HX and the A100, and measured 350 s per step at 16:9 1 MP with the standard model, without flash attention or Comfy Kitchen. Use the INT8 ConvRot model instead.
Reported
Source: @meatsus, 2026-08-26
SageAttention 2 built from source, and vllm-omni instead of ComfyUI
Smit (2026-09-06) found INT8 MiniMax H3 indistinguishable from BF16 in his tests. SageAttention 2 works on the card if built from source and is the fastest option ("int8 + sage and it fliiiies"). He uses a T2V flow; his Telegram bot renders on two 170HX cards while Gemma 4 on a spare 3090 rewrites prompts and 4 agents sanity-check them. For comparison, H3 split over 4 NVLinked A100 SXM4 cards used about 22 GB per card. On 2026-09-07 he found H3 runs on vllm-omni without hassle and replaced his ComfyUI backend with it for a fixed pipeline: two vllm-omni workers for H3 and one for the Gemma 4 prompt enhancer, which needed some tinkering.
Reported
Source: @smit1237, 2026-09-06
Source: @phoenixone7, 2026-09-06
Source: @roger_dammit, 2026-09-08
One video job per 64 GB card; system RAM is for cache overflow, not for the model
Sojun runs video generation as one job per card in separate processes (2 at once, soon 4) on an H12D-8D board with 8x 64 GB DIMMs. He finds 64 GB of VRAM very comfortable for video generation, MiniMax H3 included; clips longer than 10 s get odd. quark2world plans 2 CMP cards plus 2 other GPUs running 4 queues in parallel and wants more system RAM for cache overflow and model or workflow swapping, not for running the model in RAM. For a single CMP running one MiniMax H3 model, 64 GB of system RAM is enough. Offloading the model itself to system RAM works but is very slow.
Reported 8 GB
Source: @sojun80, 2026-08-26
Source: @quark2world, 2026-08-26
Source: @_arcanum_, 2026-08-26
Raylight for multi-GPU ComfyUI on CMP cards (untested)
On 2026-08-18 Mixtre.sh suggested Raylight, which adds multi-GPU acceleration to ComfyUI ("you can use several video cards together for overall acceleration"), as a way to use several CMP cards together for image and video generation, and planned to test it first. No results were posted. Others note that diffusion does not really distribute across cards (see the early H3 reports below).
Theory
Source: repo github.com/komikndr/raylight by komikndr on GitHub, posted by @mixtresh, 2026-08-18
Video generation
MiniMax H3 default ComfyUI workflow on a 64 GB card: 126 s at 864x480 with models loaded
ju5t5omedude (64 GB card) ran the MiniMax H3 default ComfyUI workflow (about 40 GB of models from Comfy-Org/MiniMax-H3 on Hugging Face, with ComfyUI's day-0 support). 864x480 took 228 s from empty and 126 s with the models loaded; an img2vid run took 135 s, then 127 s. At higher resolution (about 720p, 0.9 MP, as Baka summarized it) a run took 429 s at about 20 s/it and used about 45 GB. The card pegs at its 250 W limit, and he suggests a 300 W VBIOS might help (Flashing the 300 W VBIOS). For comparison a 3060 needed 25 to 72 minutes per default generation. He found LTX about 5x faster than H3, and image generation "very fast".
Reported 8 GB
Source: @ju5t5omedude, 2026-08-04
Source: @auxiliary_baka, 2026-08-04
Early single-card H3 reports: 53 s/step at 720p, half an RTX Pro 6000, 1.0 MP for 15 s without OOM
bayley_w (2026-08-09) ran MiniMax H3 on one 170HX as his first card's burn-in, at about 53 s per step at 1280x720 for 10 s clips. A full episode took nine hours of H3 rendering, with the screenplay written by an agent on a local GLM 5.2 (about 35 million tokens); H3 handled silent prompts badly and its audio was weak. He offered his SageAttention patches (about 15% faster). DaLZy: one 170HX is enough for H3, at about 1 h of compute per 15 s clip, against MiniMax's official price of 8 cents per second. Asked on 2026-08-17 how H3 runs, bayley_w said about half the speed of an RTX Pro 6000, and ju5t5omedude that it runs easily but is unoptimized and likely well behind Blackwell. A 3090 runs out of memory at 0.7 MP on 20 s clips (1a2b3c0967), while clee (2026-08-30) has done 1.0 MP for 15 s without OOM on a single 170HX. ju5t5omedude called it the best diffusion card at its price and noted diffusion cannot really be distributed across cards.
Reported
Source: @dalzyyy, 2026-08-09
Source: @bayley_w, 2026-08-09
Source: @quark2world, 2026-08-09
Source: @.philow, 2026-08-17
Source: @ju5t5omedude, 2026-08-17
Source: @1a2b3c0967, 2026-08-30
Source: @c133, 2026-08-30
H3 INT8 ConvRot fully in VRAM: a 5 s 960x544 clip in about 200 s
Smit (2026-08-19) said INT8 MiniMax H3 beats his 3090 by a lot: 960x544, 5 s, 20-step clips take about 200 s on one unlocked card. He blamed heavy thermal throttling on a low-static-pressure fan and hardened thermal compound (Fans and ducts, Thermal interface: paste, pads and phase-change). On 2026-08-21 he said this cut his video generation cost about 10x; clips are capped at about 15 s, and omni reference (images, audio and the previous 4 to 5 s of video for continuation) costs extra compute. Harmacist: the CMP is "really fast" with H3 and matches what he has seen from 5090 users. Lycorascence: it matches his 3090 when the job fits in VRAM; slowdowns come mainly from the slower PCIe link and from keeping VRAM use low to stay clear of the protected region (Xid 31 and the reserved top of VRAM); longer videos can be stitched from 5 to 10 s clips with ComfyUI director or continuation nodes. Smit (2026-08-22): plain int8 convrot fits fully in 170HX VRAM with zero offload and uses the tensor cores; he did not use the turbo LoRA ("turns H3 into Wan 2.2") or Kitchen Attention; int8convrot requires CUDA 13. Lycorascence used int8convrot with ck-attention, 32 steps of dpm2++a (about 2x compute per step) at about 1376x768 (0.75 to 0.8 MP): a 50 s scene took around 6 h of total compute, and the newer sparse attention node brought 5 to 10 s segments at 30+ steps down to 30 to 45 min. Smit's pipeline runs a user prompt through a Gemma 4 prompt enhancer and a 4-agent refiner, then H3.
Reported
Source: @smit1237, 2026-08-19
Source: @romangitz, 2026-08-21
Source: @lycorascence, 2026-08-21
Source: @ronnyfish, 2026-08-22
H3 on a 10 GB card at 40 GB in an x1 slot: about even with a lane-restricted 3090
Lycorascence (2026-08-26) runs MiniMax H3 on his 10 GB card at 40 GB (Memory unlock on the 10 GB card) after patching the workflow so it no longer overwrites its buffer and crashes the card. At 16:9 0.5 MP, draft or moderate step counts take 5 to 10 minutes with int8 convrot. Side by side at 1 MP, an unmodded x1 CMP at 40 GB and a somewhat lane-restricted 3090 run about even on s/it; Dino saw the same, with the CMP a couple of seconds ahead. quark2world thinks H3 is better served by a 4090 or 5090.
Proven 10 GB
Source: @lycorascence, 2026-08-26
Source: @quark2world, 2026-08-27
Source: @dino_38975, 2026-08-29
MiniMax H3 render times from three users on one card
Smit (2026-09-13): an 8 s 720p clip takes about 15 minutes on one 170HX; a turbo LoRA would be faster, but he chooses not to use it. Phoenix17: a 30 s 640x960 clip at 24 fps (then RIFE to 48 fps) takes about 13 to 14 minutes on one card. Sojun: a 10 s clip at about 1024x7xx takes about 3 minutes, and H3 gets "wonky" past 10 s. Sojun runs 4 cards to make 4 renders at once and pick the best, and Smit serves a bot the same way with 2 cards. Phoenix17's prompting tips: above a certain resolution the model stops following prompts, and for long clips direct each few-second segment.
Proven
Source: @smit1237, 2026-09-13
Source: @phoenixone7, 2026-09-13
Source: @sojun80, 2026-09-13
30 s H3 clips on one card: from about 22 to about 13 minutes with VDN and INT8 ConvRot (September)
Phoenix17 (2026-09-05) ran H3 video diffusion in ComfyUI at INT8 on one CMP: 30 s clips at 640x960 in about 22 minutes, saying the model "feels like it was designed for an 80GB card". A second card ran vLLM with Qwen 3.8 27B (top_p lowered from 0.95 to 0.90) in an automated loop that writes prompts, checks the first frame and the workflow debug logs, and edits the workflow; it edited ComfyUI workflows better than DeepSeek V4 Flash. His tips: Sol Attention plus the MiniMax H3 Cache, and a higher-resolution first-frame image at the same aspect ratio for sharpness. ComfyUI ships an H3 template, but alanoo says its pruned model degrades quality (fine for low-VRAM cards).
On 2026-09-07 he cut the 30 s generation from about 22 to about 13 minutes with OpenVDN vdn-minimax-h3, Comfy's upgraded Kitchen Attention and an INT8 convrot model (drbaph/vdn-minimax-h3-int8-convrot-comfyui; Saganaki22/ComfyUI-VDN-H3), with no visible quality loss: about 14 minutes for 30 s of I2V at 640x960 (about 4 minutes of it outside sampling), with VRAM at about 47 to 60 GB on one card. 12 steps plus a 6-step H3 cache gave better temporal consistency than 8 + 2 (about 1 minute slower), while 4 steps lost audio sync and made a hand vanish. roger_dammit hit OOM until he used an INT8 convrot model as the main model. On 2026-09-16 Phoenix17 generated about 30 s at 960x640 in about 17 minutes for the whole workflow (13 to 14 of it generation), dropping to about 14 minutes after flashing VBIOS 92.00.6D.00.0A and setting NDIV 72 (VBIOS and firmware, 170tune).
Reported 8 GB
Source: @phoenixone7, 2026-09-05
Source: @alanoo, 2026-09-05
Source: @roger_dammit, 2026-09-07
Source: @wakeupmbrc1, 2026-09-16
An 8 s H3 clip in 10 to 15 minutes on an unlocked 8 GB card; 30 s at 640x960 in about 15 minutes (October)
domrockt2 (i5-14600K, 128 GB DDR5) uses the unlocked 8 GB card for video generation: about 10 to 15 minutes for an 8 s MiniMax H3 video. Phoenix17 generates 30 s clips at 640x960 on a single CMP with his own H3 model and I2V workflow in about 15 minutes, 10 of them in H3. Longer clips do not fit on one CMP; he runs video workflows near the 64 GB edge and says the models feel designed for 80 GB cards. Passing a larger, more detailed first frame to H3 preserves detail.
Reported 8 GB
Source: @domrockt2, 2026-10-07
Source: @lenxtroz, 2026-10-08
Source: @phoenixone7, 2026-10-08
LTX 2.3: about 24 s of upscaled 48 fps video in 6 to 8 minutes on one card
Phoenix17 (2026-09-02) generates about 24 s of LTX 2.3 video, spatially upscaled to 896x1152 and interpolated to 48 fps, in as little as about 350 s on one card; the full workflow takes about 6 to 8 minutes depending on decode handling. A similar workflow took about 15 minutes on his RTX 5080 mobile, and about 12 minutes on the CMP with a higher-quality model before further tuning. He shelved H3 at that point because its audio is weaker than LTX 2.3 and later.
Reported
Source: @phoenixone7, 2026-09-01
LTX against H3: lighter, a better latent upscaler, worse physics
Phoenix17 (2026-10-04): LTX is worse than H3 in physics and choreography but much lighter; its latent upscaler is better than H3's and it allows higher output resolution. He posted an LTX 2.5 I2V sampler comparison. Miséricorde pointed to an alpha-matte LoRA for LTX, useful for FX sprites.
Reported
Source: @phoenixone7, 2026-10-04
Source: @wakeupmbrc1, 2026-10-04
Overnight batch diffusion: LTX 2.5, MiniMax H3 and Qwen 2.1 at BF16
Louis runs large overnight batch jobs with Qwen 2.1 and LTX 2.5 at BF16 on his cards and says they are great for diffusion models, though the models still trail Seedance on coherence and small faces. Sojun has never needed more than 64 GB for MiniMax H3. mrpandagod asked about the MiniMax fasth3 runner in xDiT for multiple cards, since interconnect bandwidth is not the bottleneck there; nobody answered. Mickey and RonnyFish rate H3 well above LTX 2.5 (face refine and motion fixer work well), and Louis notes that LTX's licensing and fine-tune frameworks matter in professional studios.
Reported
Source: @louis7132, 2026-09-23
Source: @sojun80, 2026-09-23
Source: @djpandaman, 2026-09-23
Source: @gpmickey, 2026-09-23
Source: @ronnyfish, 2026-09-23
MiniMax H3 prompting: positive prompts only, scene by scene, and an upscaled start image
Phoenix17 (2026-09-08): use positive prompts only with H3, because it samples negative prompts as if they were positive ("no balloons in the sky" makes balloons more likely), and describe the video scene by scene, soundscape included. Upscale the start image about 4x before I2V for sharper results; to upres low-resolution source images, run them through an image model at a denoise of about 0.55. H3 audio quality varies with seed, sampling and attention configuration. His ranking: WAN is the worst; LTX 2.3 has the most LoRAs; LTX 2.5 is the fastest and slightly better than 2.3; both LTX versions beat H3 on facial expressions and voice acting, but H3 is better at everything else, animation included.
Reported
Source: @phoenixone7, 2026-09-08
Source: @roger_dammit, 2026-09-08
Source: @catchyuser, 2026-09-08
A full music video made with MiniMax H3 on one CMP 170HX
quark2world produced a full music video ("Glitch - Never catch my Face") with MiniMax H3 on a CMP 170HX, generating 10 to 12 variants per shot to get lip and head motion right; MiniMax can also generate audio. Reference images came from gpt-image-1.5 and Krea / Z-Image Turbo, and the song from Suno 5.5.
Reported
Source: @quark2world, 2026-10-04
Source: @quark2world, 2026-10-06
Idea: MiniMax H3 with a smaller text encoder on a single CMP 90HX
MrLoki89 shared a r/StableDiffusion post about running MiniMax H3 with a 4B or 8B text encoder instead of the 32B one, and suggested this could make H3 usable on a single CMP 90HX (CMP 90HX unlock hub). No results were posted.
Theory
Source: @serplay228, 2026-08-14
Image generation
Z-Image Turbo at 1024x1024 in under 4 s; about 100 s faster than an RTX 5060 Ti on a detailed workflow
Waka324 (2026-08-26) reports image generation "screams" on the CMP 170HX in ComfyUI compared with his RTX 3090, V100 and RTX 8000: a 1024x1024, 8-step Z-Image Turbo image takes under 4 s against about 7 to 8 s on a 3090, which he puts down to HBM bandwidth. He tried ideogram v4 FP8 and planned INT8 and flux2.dev next. On a detailed ComfyUI text-to-image workflow Lenxtroz (2026-09-15) found the 170HX about 100 s faster than an RTX 5060 Ti 16GB and planned to sell the 5060 Ti; regaly suggested keeping it because a Qwen flash model runs at the same speed on the 5060 Ti with expert and n-gram offloading, but the two cards do not fit in his case together. MrManny (2026-09-28), with 2x 170HX and a 5090, asked for video generation guides and Takka suggested H3. Miséricorde (2026-10-01, in a thread with Busulle) confirmed that image and video generation work on these cards.
Reported
Source: @cmdrwaka324, 2026-08-26
Source: @lenxtroz, 2026-09-15
Source: @reggie7756, 2026-09-15
Source: @mrmannyatx, 2026-09-28
Source: @spectakkular, 2026-09-28
Source: @busulle, 2026-10-01
Source: @wakeupmbrc1, 2026-10-01
Qwen Image 2.1 needs about 40 GB; Krea 2 still better for anime and comics
Qwen Image 2.1 takes about 40 GB of VRAM and drifts characters when given more than 2 reference images; Krea 2 still does better anime and comic output. Sojun calls Qwen Image 2.1 the best open-source image model by far. Takka finds Qwen 2.1 fine for image editing, though he struggled to reach the published quality.
Reported
Source: @quark2world, 2026-10-04
Source: @sojun80, 2026-10-04
Source: @djpandaman, 2026-10-04
Source: @licensedterrapin, 2026-10-06
Source: @spectakkular, 2026-10-06
Krea 2's Qwen3-VL 4B text encoder stops at 512 tokens
Phoenix17 found that Qwen3-VL 4B, used as the text encoder (CLIP) for Krea 2, has a hard limit of 512 tokens, which causes prompt-adherence problems on longer prompts. Keep Krea 2 prompts short.
Reported
Source: @phoenixone7, 2026-09-19
Voice and audio
Breeze for one-shot voice cloning
Takka's pick for one-shot voice cloning on the card is Breeze: lightweight and very good.
Reported
Source: @licensedterrapin, 2026-10-06
Source: @spectakkular, 2026-10-06
Audio from the video models: LTX beats MiniMax H3 on voice
MiniMax H3 generates audio with the video, but owners rate it below LTX. bayley_w found H3's audio weak (2026-08-09). Phoenix17 shelved H3 for a while because its audio is weaker than LTX 2.3 (2026-09-02), found it worse than LTX 2.3 and 2.5 (2026-09-05), and notes H3 audio quality varies with seed, sampling and attention configuration, with both LTX versions ahead on facial expressions and voice acting (2026-09-08). For music, quark2world generated the song for his H3 music video with Suno 5.5 rather than with H3.
Reported
Source: @bayley_w, 2026-08-09
Source: @phoenixone7, 2026-09-01
Source: @phoenixone7, 2026-09-05
Source: @phoenixone7, 2026-09-08
Source: @quark2world, 2026-10-06
Why run it locally, and other non-LLM uses
Video, audio and image models are where local beats the API on cost
quark2world (2026-08-09): at 4 cards the energy cost alone would pay for the most expensive AI subscriptions, so the cards make most sense for video, audio and image models, whose APIs are still expensive. Phoenix17: running locally is private, cannot easily be taken away, and is much cheaper for video generation (Kling charges for bad renders). By 2026-09-19 he had generated over 1,000 videos at 960x640, 30 s each, on his CMP, estimating about 1,000 USD at API prices against under 50 USD of electricity; he considers image and video generation the clear case where local beats the API on cost.
Reported
Source: @quark2world, 2026-08-09
Source: @phoenixone7, 2026-08-09
Source: @phoenixone7, 2026-09-19
Protein folding and bioinformatics at full power for days
mrpandagod (2026-09-01) works on bioinformatics and protein folding, for which he finds the 170HX incredible value. Skyflyingfish uses two 170HX cards (with a 5090) for protein design at the full 250 W for 24 to 48 hours at a time and favours water cooling (Water cooling). Fitting two 170HX cards and a 5090 in one PC is nearly impossible; robbbbbb7973 has 3 cards on the floor because his 5090 blocks 2 slots.
Reported
Source: @skyflyingfish, 2026-08-08
Source: @robbbbbb7973, 2026-08-08
Source: @djpandaman, 2026-09-01
How it compares for diffusion
LLM comparisons are on How the 170HX compares.
Early expectations: "total garbage" reputation vs "about 4090 speed with 3x the VRAM"
Contested
- quark2world, who works with H200 and B300 systems, said that in his image pipelines the RTX 6000 was only about 30% faster than the A100, while the 4090 and 3090 were slower than the A100 thanks to its larger batch sizes, and expected the unlocked CMP to behave like the A100. Source: @quark2world, 2026-07-19
- Luna expected it to be poor for Stable Diffusion. Source: @lunaroseoc, 2026-07-19
- DeeJay: about RTX 4090 speed with 3x the VRAM, "the defacto king for image and video generation"; he bought two to run two ComfyUI containers on high-res video at once. Source: @deejay9983, 2026-07-30
- Sojun, testing an unlocked card in ComfyUI, called it "wildly fast" and said it might be beating a 5090. Source: @sojun80, 2026-07-30
- fallengod had heard the 170HX was "total garbage" at image generation (its reputation before the unlock). Source: @fallengod9962, 2026-07-30
- ju5t5omedude had not been impressed with video workflows and asked for DeeJay's workflow. Source: @ju5t5omedude, 2026-07-30
No shared benchmark numbers were posted in that thread; the measured numbers came later (table at the top).
About two CMPs per RTX 5090 for image and video generation
ju5t5omedude found LTX 2.3 runs well, roughly 2 minutes for about a 30 s clip, with no optimization. DeeJay compared rented 170HX cards against his 3090s in ComfyUI (screenshots) and called the card "the de facto King of image and video generation" for value; he argues 2 of these beat one 5090 for that workload: a little slower, similar power draw, and two concurrent ComfyUI containers. Miséricorde (2026-09-01): an RTX 5090 draws at least 400 W generating one image, while two CMPs comfortably run at about 360 W total generating two in parallel, each CMP taking roughly 30% longer per image than the 5090. No model, resolution or settings were given.
Reported
Source: @ju5t5omedude, 2026-07-24
Source: @deejay9983, 2026-07-24
Source: @wakeupmbrc1, 2026-09-01
A 5090 is about 30 to 40% faster for diffusion; the CMP is no desktop card
Miséricorde (2026-09-16) finds CMP diffusion 30 to 40% slower than a 5090 on MiniMax and LTX because Ampere has no native FP8, but with less offload and better quants per GPU, and core plus HBM overclocking speeds it up (Undervolting and power limits). On 2026-09-20 he repeated that a 5090 is only about 30 to 40% faster than a CMP for diffusion, with much less VRAM. _L_ said the CMP is very slow for gaming and workstation work and no daily driver, so she kept her 5090 plus a separate 4x CMP 170HX LLM server. Miséricorde put an RTX 5070 in his desktop and runs his CMPs in a Huanan board over SSH.
Reported
Source: @wakeupmbrc1, 2026-09-16
Source: @cibernox, 2026-09-20
Source: @lia_oc, 2026-09-20
Source: @wakeupmbrc1, 2026-09-20
A video-generation chart against Blackwell, disputed as benchmaxed
Contested
- aaasss posted a Chinese-language video generation timing comparison in minutes and seconds, which Baka re-posted translated (translated from Chinese); aaasss summarized the 170HX as 1/10 the price for 70% of the performance. Source: @aaasss1456, 2026-08-05
Source: @auxiliary_baka, 2026-08-05 - ju5t5omedude called it benchmaxed: on the same prompt and model, int8 against int8, his 170HX was more than twice as slow as a Blackwell card. Source: @ju5t5omedude, 2026-08-05
- DeeJay noted that Blackwell's FP4 tensor cores help 4-bit models. Source: @deejay9983, 2026-08-05
Training
Training on CMPs: "awful" vs "fine with x16 lanes"; single-card LoRA works
Contested 8 GB
- machinezero: training or fine-tuning needs PCIe bandwidth for the data loader and checkpointing, so this GPU would be awful at it; its value is inference, thanks to memory bandwidth. Jeremy (Ends) agreed tensor parallelism is a dead end while pipeline-parallel MoE inference is fine, and giaki3003 said the cards cannot be used for training at this bandwidth. Source: @machinezer0, 2026-07-29
Source: @endsjeremy, 2026-07-29
Source: @giaki3003, 2026-07-29 - CUTE disagreed in part: you can train on a single card with the right settings; multi-card training loses a lot of efficiency on PCIe 2.0, and with Gen4 he would see no big problem. Source: @cute_sc2, 2026-07-29
- turbine: 4x 170HX make a pretty good DDP training machine (no numbers). Source: @turbine, 2026-08-17
- Smit trained a MiniMax H3 LoRA on an unlocked card at about 1 it/s (2026-08-19), later a LoRA on 400+ images in about 1 h 30 min that learned every unusual concept he fed it (2026-09-06), and ran 12 h of heavy LoRA training on a Gemma 4 card without failure. Source: @smit1237, 2026-08-19
- DaLZy did training on a rented 6-card rig and it went badly: he woke up to the 6 cards at 60 W, starved. RedPandaMining, lrdimm and segmond see the cards as inference only; segmond called them useless for training unless the job fits on one GPU, or until PCIe 4 x16. Source: @dalzyyy, 2026-08-29
Source: @redpandamining, 2026-08-29
Source: @lrdimm, 2026-08-29
Source: @segmond, 2026-08-29 - giaki3003: training is not feasible even at Gen2 x16 without NVLink (NVLink). Source: @giaki3003, 2026-08-30
- gleb.shnshn: single-GPU performance looks decent. Source: @gleb.shnshn, 2026-08-30
- Phoenix17: MoE or sparse models can be trained, for example a 30B MoE with 1 to 4B experts on 4 cards (untested). Source: @phoenixone7, 2026-08-30
- Harmacist got a 2x training speedup by switching to an INT8 forward and backward pass. Source: @romangitz, 2026-08-30
- DeeJay: the cards would not be good for training, given limited PCIe and no NVLink. bayley_w disagreed: they are fine with FSDP or DDP when the x16 lanes are populated (PCIe x16 capacitor mod), though not with Megatron tensor parallel; deferring gradient syncs gains more speed; he fine-tunes robotics models on his cards with performance similar to a 5090, twice the VRAM and half the price. Source: @deejay9983, 2026-09-01
Source: @bayley_w, 2026-09-01
How the link limits multi-card work in general: Parallelism over the PCIe link.
Single-card training speed: about 70% of a 5090 (gym) vs almost 4x slower (programmerboi)
Contested
- gym (2026-07-31) has the cards working in training workloads: single-GPU training reaches about 70% of an RTX 5090, with much of the gap from PCIe 1.0 x4 data loading (he tested over a Thunderbolt 3 eGPU in a VM), and he expects Gen2 x16 to narrow it. For multi-GPU data-parallel LoRA only the gradient sync needs PCIe bandwidth, so it should be workable with the capacitor mod. On 2026-08-05 he said he fine-tunes multiview (VGGT-type) transformers on the cards; single-card performance is not far behind a 5090, and multi-GPU fine-tuning tolerates the slow interconnect because gradients are small, though he would want full Gen2 x16 rather than x4 for LoRA and expects pretraining from scratch to be communication bound. mfj4 noted that full fine-tuning of small LLMs needs more than 64 GB, so two cards. Source: @jimtjames, 2026-07-31
Source: @jimtjames, 2026-08-05
Source: @mfj4, 2026-08-05 - programmerboi tested a single CMP against a 5090 for training: almost 4x slower, on a single card so with no PCIe x4 bottleneck. A 5090 with CPU offload beat the 170HX without offload; DeepSpeed ZeRO-3 with optimizer and activation offload is faster; his 230 USD V100 trains about as well. He suspects there is no optimized GEMM kernel for the CMP's harvested Ampere chip. Source: @programmerboi, 2026-10-14
- orrorin did SFT on 4x CMP: slow; during offload the stats show the PCIe 2.0 x4 bandwidth as the bottleneck, so the x16 mod is essential. For serious training, prototype locally and rent a box. Sojun has trained small models on the 64 GB cards: the results were poor, but it works. matstatman and Lenxtroz took part in the thread. Source: @_orrorin, 2026-10-16
Source: @matstatman, 2026-10-16
Source: @sojun80, 2026-10-16
Source: @lenxtroz, 2026-10-16
Idea: fine-tuning a 9B to 12B model on one card
DaLZy (2026-08-09) thinks one 170HX should be enough to experiment with fine-tuning Qwen 9B or Gemma 12B. He had not tried it.
Theory
Source: @dalzyyy, 2026-08-09
Gaussian splat training is a poor fit (low FP32 throughput)
robbbbbb7973 asked about training Gaussian splats (for example LichtFeld Studio). gym explained that splats have few dense matmuls and generally use FP32 rather than BF16, so they run on the standard CUDA cores, not the tensor cores. The 170HX FP32 throughput is low (around 12 TFLOPS, roughly a 3060), so despite its memory bandwidth, splat workloads favour consumer GPUs (4090, A6000). Expect poor splat performance; nobody has measured it yet (Compute, data types and missing features).
Theory 8 GB and 10 GB
Source: @robbbbbb7973, 2026-07-20
Source: @jimtjames, 2026-07-20
Open questions
- A shared image and video benchmark (SDXL, Flux, Z-Image Turbo, MiniMax H3, LTX) on one 170HX against a 3090, 4090, 5090 and a Blackwell card, with the same prompt, model and INT8 quant, would settle the early claims and the disputed Blackwell chart.
- Training throughput, single card vs multi-card (FSDP or DDP, and a 30B MoE with 1 to 4B experts on 4 cards), at x4 and at x16 with a named model, against a 5090.
- Raylight multi-GPU ComfyUI on several CMP cards; MiniMax H3 with a 4B or 8B text encoder on a single CMP 90HX or 170HX; Gaussian splat training (LichtFeld Studio) on one 170HX against a consumer GPU.
Related
How the 170HX compares · Quantization on the 170HX · Compute, data types and missing features · Parallelism over the PCIe link · PCIe x16 capacitor mod · NVLink · 8 GB card · 10 GB card · Memory unlock on the 10 GB card · Xid 31 and the reserved top of VRAM · Flashing the 300 W VBIOS · VBIOS and firmware · 170tune · Undervolting and power limits · Fans and ducts · Thermal interface: paste, pads and phase-change · Water cooling · CMP 90HX unlock hub · Other CMP cards and other NVIDIA GPUs · Engines · Benchmarks · Recipes