gemma 3 4b it needs ~4.9 GB VRAM. RTX 2070 Super 8GB has 8.0 GB. With Q4_K_M quantization, expect ~56 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
56.0 tok/s
TTFT
3457 ms
Safe context
122K
Memory
4.9 GB / 8.0 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 | 56.0 tok/s | 1886 ms | 122K |
| Coding | C | Runs well | 56.0 tok/s | 3457 ms | 122K |
| Agentic Coding | C | Runs well | 56.0 tok/s | 5029 ms | 122K |
| Reasoning | C | Runs well | 56.0 tok/s | 4086 ms | 122K |
| RAG | C | Runs well | 56.0 tok/s | 6286 ms | 122K |
Inference speed
Estimated decode speed (tokens/sec) for gemma 3 4b it at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~76 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 | 76.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 64.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 64.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 56.0 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 56.0 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 56.0 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 56.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 56.0 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 56.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 56.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 56.0 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 56.0 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 56.0 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 56.0 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 56.0 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 56.0 | 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 gemma 3 4b it (4B params) fits at each quantization level on RTX 2070 Super 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.6 GB | Low | C51 |
Q3_K_S | 3 | 2.0 GB | Low | C52 |
NVFP4 | 4 | 2.2 GB | Medium | C52 |
Q4_K_M | 4 | 2.4 GB | Medium | C53 |
Q5_K_M | 5 | 2.9 GB | High | C54 |
Q6_K | 6 | 3.3 GB | High | C54 |
Q8_0Best for your GPU | 8 | 4.3 GB | Very High | C53 |
F16 | 16 | 8.2 GB | Maximum | F0 |
Copy-paste commands to run gemma 3 4b it on your machine.
Run
lms load hf-maziyarpanahi--gemma-3-4b-it-gguf && lms server startYes, RTX 2070 Super 8GB can run gemma 3 4b it with a C grade (Runs well). Expected decode speed: 56.0 tok/s.
gemma 3 4b it (4B parameters) requires approximately 4.9 GB of memory with Q4_K_M quantization.
The recommended quantization for gemma 3 4b it is Q4_K_M, which balances quality and memory efficiency.
On RTX 2070 Super 8GB, gemma 3 4b it achieves approximately 56.0 tokens per second decode speed with a time-to-first-token of 3457ms using Q4_K_M quantization.
For coding workloads, gemma 3 4b it on RTX 2070 Super 8GB receives a C grade with 56.0 tok/s and 122K context.
On RTX 2070 Super 8GB, gemma 3 4b it can safely use up to 122K 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-maziyarpanahi--gemma-3-4b-it-gguf-on-rtx-2070-super-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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