MacBook Pro M4 Max 48GBコスパ重視
48 GB Unified (+16)
C
This setup is broadly balanced for this model.87.8 tok/sデコード
〜$2,499 MSRP
Can Vicuna 7B run on Radeon AI PRO R9700 32GB?
YES — Runs Great
C53Usable
Estimated from fit model
Vicuna 7B needs ~16.2 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~88 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 16.2 GB, 88.4 tok/s, Runs well
16.2 GB required32.0 GB available
51% VRAM used
Fit status
Runs well
Decode
88.4 tok/s
TTFT
2189 ms
Safe context
4K
Memory
16.2 GB / 32.0 GB
Memory breakdown
Weights4.3 GB
KV Cache7.8 GB
Runtime0.9 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsVicuna 7B on Radeon AI PRO R9700 32GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 88.4 tok/s decode · 2.2s TTFT (warm) · 221 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 88.4 tok/s | 1194 ms | 4K |
| Coding | C | Runs well | 88.4 tok/s | 2189 ms | 4K |
| Agentic Coding | B | Runs well | 88.4 tok/s | 3184 ms | 4K |
| Reasoning | C | Runs well | 88.4 tok/s | 2587 ms | 4K |
| RAG | B | Runs well | 88.4 tok/s | 3981 ms | 4K |
Quantization options
How Vicuna 7B (7B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C44 |
Q3_K_S | 3 | 3.4 GB | Low | C44 |
NVFP4 | 4 | 3.9 GB | Medium | C44 |
Q4_K_M | 4 | 4.3 GB | Medium | C44 |
Q5_K_M | 5 | 5.0 GB | High | C44 |
Q6_K | 6 | 5.7 GB | High | C44 |
Q8_0 | 8 | 7.5 GB | Very High | C45 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | C48 |
Get started
Copy-paste commands to run Vicuna 7B on your machine.
Run
ollama run vicunaアップグレードオプション
Vicuna 7Bを快適に動かすハードウェア
Frequently asked questions
Can Radeon AI PRO R9700 32GB run Vicuna 7B?
Yes, Radeon AI PRO R9700 32GB can run Vicuna 7B with a C grade (Runs well). Expected decode speed: 88.4 tok/s.
How much VRAM does Vicuna 7B need?
Vicuna 7B (7B parameters) requires approximately 16.2 GB of memory with Q4_K_M quantization.
What is the best quantization for Vicuna 7B?
The recommended quantization for Vicuna 7B is Q4_K_M, which balances quality and memory efficiency.
What speed will Vicuna 7B run at on Radeon AI PRO R9700 32GB?
On Radeon AI PRO R9700 32GB, Vicuna 7B achieves approximately 88.4 tokens per second decode speed with a time-to-first-token of 2189ms using Q4_K_M quantization.
Can Radeon AI PRO R9700 32GB run Vicuna 7B for coding?
For coding workloads, Vicuna 7B on Radeon AI PRO R9700 32GB receives a C grade with 88.4 tok/s and 4K context.
What context window can Vicuna 7B use on Radeon AI PRO R9700 32GB?
On Radeon AI PRO R9700 32GB, 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.
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<iframe src="https://willitrunai.com/embed/vicuna-7b-on-radeon-ai-pro-r9700-32gb" 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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