Radeon AI PRO R9700 32GBBest value
640 GB/s (+490)
C
Raises estimated decode speed by about 245%.88.4 tok/s decode
Raises estimated decode speed by about 245%.
Adds memory headroom for longer context windows and future model growth.
~$1,899 MSRP
Vicuna 7B needs ~16.9 GB VRAM. MacBook Pro M3 Pro 36GB has 25.9 GB. With Q4_K_M quantization, expect ~26 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
25.6 tok/s
TTFT
7550 ms
Safe context
4K
Memory
16.9 GB / 25.9 GB
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 25.6 tok/s | 4118 ms | 4K |
| Coding | C | Runs well | 25.6 tok/s | 7550 ms | 4K |
| Agentic Coding | C | Runs with offload | 25.6 tok/s | 10981 ms | 4K |
| Reasoning | C | Runs well | 25.6 tok/s | 8922 ms | 4K |
| RAG | C | Runs with offload | 25.6 tok/s | 13726 ms | 4K |
Inference speed
Estimated decode speed (tokens/sec) for Vicuna 7B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 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 | 98.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 98.0 | Tight |
| RTX 3090 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 98.0 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 87.8 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 87.8 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 56.2 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 51.5 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 45.3 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 43.0 | Heavy offload |
| RTX 3060 12GB | 12 GB | Q4_K_M | 24.7 | Heavy offload |
| RTX 4060 8GB | 8 GB | Q4_K_M | 10.6 | 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 Vicuna 7B (7B params) fits at each quantization level on MacBook Pro M3 Pro 36GB (25.9 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C44 |
Q3_K_S | 3 | 3.4 GB | Low | C45 |
NVFP4 | 4 | 3.9 GB | Medium | C45 |
Q4_K_M | 4 | 4.3 GB | Medium | C45 |
Q5_K_M | 5 | 5.0 GB | High | C45 |
Q6_K | 6 | 5.7 GB | High | C46 |
Q8_0 | 8 | 7.5 GB | Very High | C47 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | C51 |
Copy-paste commands to run Vicuna 7B on your machine.
Run
ollama run vicunaUpgrade options
Raises estimated decode speed by about 245%.
Adds memory headroom for longer context windows and future model growth.
~$1,899 MSRP
Raises estimated decode speed by about 243%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Yes, MacBook Pro M3 Pro 36GB can run Vicuna 7B with a C grade (Runs well). Expected decode speed: 25.6 tok/s.
Vicuna 7B (7B parameters) requires approximately 16.9 GB of memory with Q4_K_M quantization.
The recommended quantization for Vicuna 7B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 Pro 36GB, Vicuna 7B achieves approximately 25.6 tokens per second decode speed with a time-to-first-token of 7550ms using Q4_K_M quantization.
For coding workloads, Vicuna 7B on MacBook Pro M3 Pro 36GB receives a C grade with 25.6 tok/s and 4K context.
On MacBook Pro M3 Pro 36GB, Vicuna 7B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.
Not always. MacBook Pro M3 Pro 36GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/vicuna-7b-on-m3-pro-36gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: