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Can the NVIDIA GB10 (128 GB) run Sarvam 105B?

71Great quality (A) · 71 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs great

Runs great. At Q4_K_M it needs about 74.8 GB of 115 GB usable, with an estimated speed of 30.1 tok/s.

Best quant
Q4_K_M
Generation speed
30.1 tok/s
Prompt processing
2,232 tok/s
First token (1K prompt)
0.5 s
Largest safe context
128K

Best app to run it on the NVIDIA GB10 (128 GB)

On Linux

LM Studio

Desktop app · runs on llama.cpp, MLX

  • Point-and-click: search, download and chat in one window.

Open Sarvam 105B in LM Studio

  • llama.cpp Usually the first to support new model architectures.
  • Ollama Runs the Hugging Face GGUF with ollama run hf.co/…

How we choose: the files published for this model, your operating system and GPU, whether it fits fully in memory, and published speed tests (MLX vs llama.cpp on Apple silicon, vLLM vs llama.cpp vs Ollama on NVIDIA). Details.

Download Sarvam 105B

Recommended for the NVIDIA GB10 (128 GB): Q4_K_M. 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 Q4_K_M file (opens in a new tab)

huggingface.co/sarvamai/sarvam-105b-gguf (opens in a new tab)

ollama run hf.co/sarvamai/sarvam-105b-gguf:Q4_K_M

Original weights · Hugging Face

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

huggingface.co/sarvamai/sarvam-105b (opens in a new tab)

No sign-in needed.

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

How it’s rated

Quality 71Great quality (A) · 71 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q4_K_M, 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 Runs great
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.32.1 / 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.25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.13.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.15.0 / 15
Match score85.1 / 100

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

Every quantization on the NVIDIA GB10 (128 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 52.5 GB 115 GB 47.0 tok/s Runs great 52Fair quality (C) · 52 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q3_K_M 62.1 GB 115 GB 38.0 tok/s Runs great 63Good quality (B) · 63 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q4_K_M 74.8 GB 115 GB 30.1 tok/s Runs great 71Great quality (A) · 71 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking. GGUF · part 1 of 9 (opens in a new tab)
Q5_K_M 85.4 GB 115 GB 25.2 tok/s Runs well 72Great quality (A) · 72 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 84/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q6_K 97.1 GB 115 GB 21.5 tok/s Runs well 74Great quality (A) · 74 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 84/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q8_0 123 GB 115 GB — Won't run 75Great quality (A) · 75 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 10/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
F16 222 GB 115 GB — Won't run 75Great quality (A) · 75 of 100: how capable the model is at F16, whatever it runs on. Match score 10/100 (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.

Other hardware that runs it

Quick answers

How much memory does Sarvam 105B need on the NVIDIA GB10 128GB?

At Q4_K_M it needs about 74.8 GB. The NVIDIA GB10 128GB has 115 GB usable.

How fast is Sarvam 105B on the NVIDIA GB10 128GB?

About 30.1 tok/s at Q4_K_M, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Sarvam 105B should I use on the NVIDIA GB10 128GB?

Q4_K_M. Of the versions that fit, it is the one that runs best; when two run equally well, the check prefers the higher-rated one.

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.

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