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Can the AMD Radeon RX 7900 XTX run Sarvam 30B?

83 ptsGreat (A) · 83 of 100 points: Model quality 29.9/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, with little memory to spare

Tight fit. At Q4_K_M it needs about 21.6 GB of 24.0 GB usable, with an estimated speed of 211 tok/s.

Best quant
Q4_K_M
Generation speed
211 tok/s
Prompt processing
31,723 tok/s
First token (1K prompt)
0.3 s
Largest safe context
128K

Download Sarvam 30B

Recommended for the AMD Radeon RX 7900 XTX: 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-30b-gguf (opens in a new tab)

ollama run hf.co/sarvamai/sarvam-30b-gguf:Q4_K_M

Original weights · Hugging Face

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

huggingface.co/sarvamai/sarvam-30b (opens in a new tab)

No sign-in needed.

Links checked 6 days ago against Hugging Face, Ollama and GitHub.

How the A grade is scored

Every result gets up to 100 points from four parts. The points decide the letter grade.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.29.9 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.24.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.15.0 / 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
Total83.9 / 10083 ptsGreat (A) · 83 of 100 points: Model quality 29.9/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15

Grade scale: S 85+ · A 72+ · B 58+ · C 42+ · D 25+ · F under 25 points.

Every quantization on the AMD Radeon RX 7900 XTX

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 14.8 GB 24.0 GB 347 tok/s Runs well 76 ptsGreat (A) · 76 of 100 points: Model quality 22.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 14.2/15 GGUF files (opens in a new tab)
Q3_K_M 17.7 GB 24.0 GB 273 tok/s Runs well 81 ptsGreat (A) · 81 of 100 points: Model quality 26.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF files (opens in a new tab)
Q4_K_M 21.6 GB 24.0 GB 211 tok/s Tight fit 83 ptsGreat (A) · 83 of 100 points: Model quality 29.9/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF · part 1 of 6 (opens in a new tab)
Q5_K_M 24.8 GB 24.0 GB 125 tok/s Runs with offload 70 ptsGood (B) · 70 of 100 points: Model quality 30.6/45, Memory fit 10.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF files (opens in a new tab)
Q6_K 28.3 GB 24.0 GB 51.1 tok/s Runs with offload 71 ptsGood (B) · 71 of 100 points: Model quality 31.2/45, Memory fit 10.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF files (opens in a new tab)
Q8_0 36.0 GB 24.0 GB 21.4 tok/s Heavy offload 61 ptsGood (B) · 61 of 100 points: Model quality 31.5/45, Memory fit 4.0/25, Speed 11.0/15, Memory use 15.0/15 GGUF files (opens in a new tab)
F16 66.3 GB 24.0 GB — Won't run 9 ptsWon't run (F) · 9 of 100 points: Model quality 9.5/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 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

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.