Sarvam 30B — Hardware Requirements & GPU Compatibility
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Sarvam 30B is a 32.2B-parameter open language model from sarvamai. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 19.63 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 16 GB (Q2_K · 64K)Best on 24 GB (Q4_K_M · ~190 tok/s · 128K)Macs from 18 GB (Q2_K · ~44 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Sarvam 30B
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
ollama run hf.co/Sumitc13/sarvam-30b-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 4.1 GB free · 16K context
Download: sarvam-30B-Q4_K_M.gguf19.58 GBSumitc13
Other ways
each opens step-by-stepRun in cloud
Fits on RTX A5000 (24 GB) (4 GB headroom) · Q4_K_M
- Generation speed
- ~152 tok/s
- generation speed
- Cost per 1M output tokens
- $0.29
- per 1M output tokens
How Much VRAM Does Sarvam 30B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 14 GB | 14.6 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 128K context · ~247 tok/s | |
| 16.0 GB | 16.6 GB | 3-bit medium — 3-bit medium quantization | Fits: 128K context · ~223 tok/s | |
| 19.6 GB | 20.2 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 128K context · ~190 tok/s | |
| 23.3 GB | 23.9 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~166 tok/s | |
| 26.9 GB | 27.5 GB | 6-bit, very good — 6-bit quantization, very good quality | Does not fit: needs 27.2 GB | |
| 32.5 GB | 33.1 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 32.8 GB | |
| 64.7 GB | 65.3 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 64.9 GB |
Selected Q4_K_M
4-bit medium quantization19.9GBat 16K context
- Weights
- 19.3 GB
- KV cache
- 0.3 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 4.1 GB free · ~190 tok/s
≥50 tok/s needs ≥224 GB/s · RTX 5090 ~293, RTX 3090 Ti ~190
Which Hardware Can Run Sarvam 30B?
Q4_K_M · 19.6 GBSarvam 30B (Q4_K_M) requires 19.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 26+ GB is recommended. Using the full 131K context window can add up to 2.5 GB, bringing total usage to 22.1 GB. 8 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 122 devices with unified memory can run Sarvam 30B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio M1 Max (32 GB).
20 GB
fits · 0.1 GB free · up to 16K ctx
1 GPU
RX 7900 XT~105Simulate output on RX 7900 XT32–40 GB
fits · 12.1 GB free · up to 128K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~293Simulate output on RTX 5090M5 Max36 GB~81Simulate output on Mac Studio M5 Max (36 GB)M532 GB~34Simulate output on MacBook Air 13" M5 (32 GB)Orin~46Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~30Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Hy Memory 30B A3BTencent | — | 19.1 GB | ~184 | same size |
| Muse Glimmer 30BMeta | — | 19.6 GB | ~36 | same size |
| NVIDIA Nemotron 3.5 Lightning 30B A3B BF16NVIDIA | — | 19.3 GB | ~197 | same size |
| Hy MT2 30B A3BTencent | — | 19.1 GB | ~170 | same size |
| EXAONE 4.5 33BLGAI-EXAONE | — | 23.9 GB | ~33 | same size |
| Gemma 4 31BGoogle | — | 22.6 GB | ~31 | same size |
Fine-tunes & variants(1)
Specifications
- Publisher
- sarvamai
- Parameters
- 32.2B
- Active parameters
- 4.5B per token
- Architecture
- SarvamMoEForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 262,144
- Release Date
- 2026-03-03
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Sarvam 30B need?
Sarvam 30B requires 19.6 GB of VRAM at Q4_K_M, or 64.7 GB at BF16. Full 131K context adds up to 2.5 GB (22.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 32.2B × 4.8 bits ÷ 8 = 19.3 GB
KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 2.8 GB (at full 131K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 4090 run Sarvam 30B?
Yes, at Q5_K_M (23.3 GB) or lower. Higher quantizations like Q6_K (26.9 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for Sarvam 30B?
For Sarvam 30B, Q4_K_M (19.6 GB) offers the best balance of quality and VRAM usage. Q5_K_M (23.3 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 14 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Sarvam 30B on a Mac?
Yes — MacBook Pro 14" M3 Pro (18 GB) and 108 other Macs can run Sarvam 30B, the smallest at Q2_K. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 19.6 GB of usable unified memory (RAM minus macOS overhead), from Mac Mini M5 Pro (24 GB).
Can I run Sarvam 30B locally?
Yes — Sarvam 30B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Sarvam 30B?
At Q4_K_M, Sarvam 30B can reach ~229 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~190 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = 1000 ÷ (active GB ÷ (bandwidth GB/s × efficiency) × 1000 + layers × routing ms)
Mixture-of-Experts: just the active experts get read for each token, along with a fixed routing cost per layer.
Example: NVIDIA B200 → 2.8 GB active ÷ (8000 × 0.65) = 0.531 ms, plus 19 layers × 0.055 ms = 1.045 ms, so 1000 ÷ 1.576 ms = ~635 tok/s
Estimated speed at Q4_K_M (19.6 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Sarvam 30B?
At Q4_K_M, the download is about 19.29 GB. The full-precision BF16 version is 64.31 GB. The smallest option (Q2_K) is 13.66 GB.
Which GPUs can run Sarvam 30B?
8 consumer GPUs can run Sarvam 30B at Q4_K_M. Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX. 1 GPU has plenty of headroom for comfortable inference.
Which devices can run Sarvam 30B?
122 devices with unified memory can run Sarvam 30B at Q4_K_M (19.6 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.