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Can the AMD Ryzen 7000 series (desktop, incl. PRO 7645 / 7745) NAS (192 GB) run Sarvam 105B?

63Good quality (B) · 63 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 70/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs well

Runs well. At Q3_K_M it needs about 66.7 GB of 173 GB usable, with an estimated speed of 11.0 tok/s.

Best quant
Q3_K_M
Generation speed
11.0 tok/s
Prompt processing
43.4 tok/s
First token (1K prompt)
23.6 s
Largest safe context
128K

Best app to run it on the AMD Ryzen 7000 series (desktop, incl. PRO 7645 / 7745) NAS (192 GB)

On Linux

llama.cpp

Command line + local API · runs on llama.cpp

  • Usually the first to support new model architectures.
llama-server -hf sarvamai/sarvam-105b-gguf:Q3_K_M

How we choose: the files published for this model, which apps run in a container on a NAS without a graphics card (models run on its processor), and whether the model fits fully in the memory models can use. Details.

Download Sarvam 105B

Quantized files · GGUF

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

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

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

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 63Good quality (B) · 63 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 70/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q3_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 well
How well it runs on the AMD Ryzen 7000 series (desktop, incl. PRO 7645 / 7745) NAS (192 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.28.4 / 45
Memory fitFull marks when the model fits in the share of the NAS’s memory that models can use (the rest stays with the NAS system and its storage services); 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.7.9 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.8.9 / 15
Match score70.1 / 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 AMD Ryzen 7000 series (desktop, incl. PRO 7645 / 7745) NAS (192 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 57.1 GB 173 GB 13.8 tok/s Runs well 52Fair quality (C) · 52 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q3_K_M 66.7 GB 173 GB 11.0 tok/s Runs well 63Good quality (B) · 63 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 70/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q4_K_M 79.4 GB 173 GB 8.5 tok/s Slow 71Great quality (A) · 71 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking. GGUF · part 1 of 9 (opens in a new tab)
Q5_K_M 90.0 GB 173 GB 7.1 tok/s Slow 72Great quality (A) · 72 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. GGUF files (opens in a new tab)
Q6_K 102 GB 173 GB 6.0 tok/s Slow 74Great quality (A) · 74 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q8_0 127 GB 173 GB 4.4 tok/s Slow 75Great quality (A) · 75 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
F16 227 GB 173 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 AMD Ryzen 7000 series (desktop, incl. PRO 7645 / 7745) NAS 192GB?

At Q3_K_M it needs about 66.7 GB. The AMD Ryzen 7000 series (desktop, incl. PRO 7645 / 7745) NAS 192GB has 173 GB usable.

How fast is Sarvam 105B on the AMD Ryzen 7000 series (desktop, incl. PRO 7645 / 7745) NAS 192GB?

About 11.0 tok/s at Q3_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 AMD Ryzen 7000 series (desktop, incl. PRO 7645 / 7745) NAS 192GB?

Q3_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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