Gemmasutra Mini 2B v1 needs ~3.3 GB VRAM. RTX 2060 6GB has 6.0 GB. With Q4_K_M quantization, expect ~28 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Fit status
Runs well
Decode
28.0 tok/s
TTFT
6914 ms
Safe context
203K
Memory
3.3 GB / 6.0 GB
This setup is broadly balanced for this model.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 28.0 tok/s | 3771 ms | 203K |
| Coding | C | Runs well | 28.0 tok/s | 6914 ms | 203K |
| Agentic Coding | C | Runs well | 28.0 tok/s | 10057 ms | 203K |
| Reasoning | C | Runs well | 28.0 tok/s | 8171 ms | 203K |
| RAG | C | Runs well | 28.0 tok/s | 12571 ms | 203K |
Inference speed
Estimated decode speed (tokens/sec) for Gemmasutra Mini 2B v1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~38 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 38.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 32.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 32.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 28.0 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 28.0 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 28.0 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 28.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 28.0 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 28.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 28.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 28.0 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 28.0 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 28.0 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 28.0 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 28.0 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 28.0 | Fits |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How Gemmasutra Mini 2B v1 (2B params) fits at each quantization level on RTX 2060 6GB (6.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.8 GB | Low | C52 |
Q3_K_S | 3 | 1.0 GB | Low | C53 |
NVFP4 | 4 | 1.1 GB | Medium | C53 |
Q4_K_M | 4 | 1.2 GB | Medium | C53 |
Q5_K_M | 5 | 1.4 GB | High | C54 |
Q6_K | 6 | 1.6 GB | High | C54 |
Q8_0Best for your GPU | 8 | 2.1 GB | Very High | C55 |
F16 | 16 | 4.1 GB | Maximum | F0 |
Copy-paste commands to run Gemmasutra Mini 2B v1 on your machine.
Run
lms load hf-thedrummer--gemmasutra-mini-2b-v1-gguf && lms server startYes, RTX 2060 6GB can run Gemmasutra Mini 2B v1 with a C grade (Runs well). Expected decode speed: 28.0 tok/s.
Gemmasutra Mini 2B v1 (2B parameters) requires approximately 3.3 GB of memory with Q4_K_M quantization.
The recommended quantization for Gemmasutra Mini 2B v1 is Q4_K_M, which balances quality and memory efficiency.
On RTX 2060 6GB, Gemmasutra Mini 2B v1 achieves approximately 28.0 tokens per second decode speed with a time-to-first-token of 6914ms using Q4_K_M quantization.
For coding workloads, Gemmasutra Mini 2B v1 on RTX 2060 6GB receives a C grade with 28.0 tok/s and 203K context.
On RTX 2060 6GB, Gemmasutra Mini 2B v1 can safely use up to 203K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-thedrummer--gemmasutra-mini-2b-v1-gguf-on-rtx-2060-6gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: