Can the NVIDIA GB10 (128 GB) run Grok-1 (314B)?

No, it doesn’t fit

No. Grok-1 (314B) needs about 138 GB even at Q2_K; the NVIDIA GB10 (128 GB) has 115 GB usable.

Download Grok-1 (314B)

Recommended: Q2_K. Every other quantization has its own download link in the table below.

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

Download the Q2_K files (opens in a new tab)

huggingface.co/Arki05/Grok-1-GGUF (opens in a new tab)

ollama run hf.co/Arki05/Grok-1-GGUF

Original weights · Hugging Face

Full-precision files from xAI, for fine-tuning, vLLM, Transformers.

huggingface.co/xai-org/grok-1 (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from xAI. The weights themselves download from Hugging Face.

github.com/xai-org/grok-1 (opens in a new tab)

git clone https://github.com/xai-org/grok-1

Links checked 3 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 35Basic quality (D) · 35 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 4/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at Q2_K, from its editorial quality tier scaled by the quantization or precision. It’s the same on any hardware that runs this variant. Scale: A+ 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Won't run: How well it runs on the NVIDIA GB10 (128 GB): speed, capped by how well it fits in memory. Runs great: 30+ tokens/s (or near the image, video or decision speed target) · Runs well: 10+ tokens/s · Slow: 3+ tokens/s · Very slow: under 3 · Won’t run: doesn’t fit even with offload. A tight fit or RAM offload caps it at Runs well; heavy offload caps it at Slow.

Match score

To order lists, each result also gets a match score out of 100 from four parts. It favors the most capable model that still runs well.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.	4.7 / 45
Memory fitFull marks when the model fits in the share of unified memory the GPU can use; fewer when it goes past that limit.	0.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	0.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	0.0 / 15
Match score	4.7 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

It doesn’t fit, so only a reduced share of the quality points counts and fit, speed and memory use score zero.

Every quantization on the NVIDIA GB10 (128 GB)

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q2_K`	138 GB	115 GB	—	Won't run	35Basic quality (D) · 35 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 4/100 (quality, fit, speed and memory use) sets the ranking.	GGUF · folder (opens in a new tab)
`Q3_K_M`	166 GB	115 GB	—	Won't run	42Fair quality (C) · 42 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 5/100 (quality, fit, speed and memory use) sets the ranking.	GGUF · folder (opens in a new tab)
`Q4_K_M`	204 GB	115 GB	—	Won't run	47Fair quality (C) · 47 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 6/100 (quality, fit, speed and memory use) sets the ranking.	GGUF files (opens in a new tab)
`Q5_K_M`	235 GB	115 GB	—	Won't run	48Fair quality (C) · 48 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 6/100 (quality, fit, speed and memory use) sets the ranking.	GGUF files (opens in a new tab)
`Q6_K`	269 GB	115 GB	—	Won't run	49Fair quality (C) · 49 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 6/100 (quality, fit, speed and memory use) sets the ranking.	GGUF · folder (opens in a new tab)
`Q8_0`	345 GB	115 GB	—	Won't run	50Fair quality (C) · 50 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 6/100 (quality, fit, speed and memory use) sets the ranking.	GGUF · folder (opens in a new tab)
`F16`	640 GB	115 GB	—	Won't run	50Fair quality (C) · 50 of 100: how capable the model is at F16, whatever it runs on. Match score 6/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)

Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.

Hardware that can run it

42Fair quality (C) · 42 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 66/100 (quality, fit, speed and memory use) sets the ranking. Apple M3 Ultra (256 GB) Q3_K_M · 12.5 tok/s · Runs well (tight fit)
35Basic quality (D) · 35 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking. Apple M2 Ultra (192 GB) Q2_K · 15.4 tok/s · Runs well (tight fit)
42Fair quality (C) · 42 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 63/100 (quality, fit, speed and memory use) sets the ranking. Apple M3 Ultra (512 GB) Q3_K_M · 12.5 tok/s · Runs well
48Fair quality (C) · 48 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking. AMD Instinct MI350X Q5_K_M · 63.2 tok/s · Runs great
47Fair quality (C) · 47 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking. AMD Instinct MI325X Q4_K_M · 59.1 tok/s · Runs great
47Fair quality (C) · 47 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GB300 Grace Blackwell Ultra Desktop Superchip Q4_K_M · 75.6 tok/s · Runs great

Models that do run on the NVIDIA GB10 (128 GB)

91Excellent quality (A+) · 91 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 95/100 (quality, fit, speed and memory use) sets the ranking. Kolibri 1 (78B-A3.5B) Q8_0 · 41.5 tok/s
86Excellent quality (A+) · 86 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 93/100 (quality, fit, speed and memory use) sets the ranking. Qwen3 Coder Next (80B-A3B) Q8_0 · 46.8 tok/s
86Excellent quality (A+) · 86 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 93/100 (quality, fit, speed and memory use) sets the ranking. Qwen3-Next 80B-A3B Thinking Q8_0 · 46.8 tok/s
84Great quality (A) · 84 of 100: how capable the model is at MXFP4, whatever it runs on. Match score 92/100 (quality, fit, speed and memory use) sets the ranking. gpt-oss 120B MXFP4 · 56.9 tok/s
84Great quality (A) · 84 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 92/100 (quality, fit, speed and memory use) sets the ranking. Qwen3 Next 80B-A3B Instruct Q8_0 · 46.8 tok/s

Quick answers

How much memory does Grok-1 (314B) need on the NVIDIA GB10 128GB?

Even at Q2_K it needs about 138 GB; the NVIDIA GB10 128GB has 115 GB usable.

What can the NVIDIA GB10 128GB run instead?

Kolibri 1 (78B-A3.5B), Qwen3 Coder Next (80B-A3B) and Qwen3-Next 80B-A3B Thinking run best on it; the page lists more, each with its speed.

What hardware can run Grok-1 (314B)?

M3 Ultra 256GB, M2 Ultra 192GB and M3 Ultra 512GB run it; the page lists more, each with its speed.

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.