Gemma 2 27B needs ~31.8 GB VRAM. Radeon Pro W7800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~16 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 with offload
Decode
16.4 tok/s
TTFT
11791 ms
Safe context
8K
Memory
31.8 GB / 32.0 GB
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 16.4 tok/s | 6431 ms | 8K |
| Coding | B | Runs with offload | 16.4 tok/s | 11791 ms | 8K |
| Agentic Coding | F | Too heavy | 6.6 tok/s | 42656 ms | 8K |
| Reasoning | B | Runs with offload | 16.4 tok/s | 13935 ms | 8K |
| RAG | F | Too heavy | 6.6 tok/s | 53320 ms | 8K |
Inference speed
Estimated decode speed (tokens/sec) for Gemma 2 27B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~58 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 | 58.2 | Offloads |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 26.9 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 26.6 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 26.6 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 22.4 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 21.3 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 20.9 | Too big |
| RTX 3090 24GB | 24 GB | Q4_K_M | 17.9 | Too big |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 16.8 | Offloads |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 12.6 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 11.6 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 10.6 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 7.5 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 3.6 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 2.3 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.0 | Too big |
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 2 27B (27B params) fits at each quantization level on Radeon Pro W7800 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 10.5 GB | Low | B66 |
Q3_K_S | 3 | 13.2 GB | Low | B67 |
NVFP4 | 4 | 15.1 GB | Medium | B68 |
Q4_K_M | 4 | 16.5 GB | Medium | B69 |
Q5_K_M | 5 | 19.4 GB | High | B69 |
Q6_KBest for your GPU | 6 | 22.1 GB | High | B68 |
Q8_0 | 8 | 28.9 GB | Very High | F0 |
F16 | 16 | 55.4 GB | Maximum | F0 |
Copy-paste commands to run Gemma 2 27B on your machine.
Run
ollama run gemma2:27bUpgrade options
Raises estimated decode speed by about 41%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Raises estimated decode speed by about 41%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Raises estimated decode speed by about 169%.
Adds memory headroom for longer context windows and future model growth.
~$10,000 MSRP
Yes, Radeon Pro W7800 32GB can run Gemma 2 27B with a B grade (Runs with offload). Expected decode speed: 16.4 tok/s.
Gemma 2 27B (27B parameters) requires approximately 31.8 GB of memory with Q4_K_M quantization.
The recommended quantization for Gemma 2 27B is Q4_K_M, which balances quality and memory efficiency.
On Radeon Pro W7800 32GB, Gemma 2 27B achieves approximately 16.4 tokens per second decode speed with a time-to-first-token of 11791ms using Q4_K_M quantization.
For coding workloads, Gemma 2 27B on Radeon Pro W7800 32GB receives a B grade with 16.4 tok/s and 8K context.
On Radeon Pro W7800 32GB, Gemma 2 27B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/gemma-2-27b-on-radeon-pro-w7800-32gb" 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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