RX 7900 XT 20GBコスパ重視
20 GB VRAM (+4)800 GB/s (+224)
B
Raises estimated decode speed by about 25%.98 tok/sデコード
Raises estimated decode speed by about 25%.
Adds memory headroom for longer context windows and future model growth.
〜$899 MSRP
Can Vicuna 7B run on RX 6950 XT 16GB?
YES — Tight Fit
C54Usable
Estimated from fit model
Vicuna 7B needs ~14.6 GB VRAM. RX 6950 XT 16GB has 16.0 GB. With Q4_K_M quantization, expect ~78 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: 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) — 14.6 GB, 78.2 tok/s, Tight fit
14.6 GB required16.0 GB available
91% VRAM used
Fit status
Tight fit
Decode
78.2 tok/s
TTFT
2474 ms
Safe context
4K
Memory
14.6 GB / 16.0 GB
Memory breakdown
Weights4.3 GB
KV Cache7.8 GB
Runtime0.9 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsVicuna 7B on RX 6950 XT 16GB
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: 78.2 tok/s decode · 2.5s TTFT (warm) · 196 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 | B | Runs well | 78.2 tok/s | 1350 ms | 4K |
| Coding | C | Tight fit | 78.2 tok/s | 2474 ms | 4K |
| Agentic Coding | F | Too heavy | 28.9 tok/s | 9740 ms | 4K |
| Reasoning | C | Tight fit | 78.2 tok/s | 2924 ms | 4K |
| RAG | F | Too heavy | 28.9 tok/s | 12175 ms | 4K |
Quantization options
How Vicuna 7B (7B params) fits at each quantization level on RX 6950 XT 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C47 |
Q3_K_S | 3 | 3.4 GB | Low | C48 |
NVFP4 | 4 | 3.9 GB | Medium | C48 |
Q4_K_M | 4 | 4.3 GB | Medium | C49 |
Q5_K_M | 5 | 5.0 GB | High | C49 |
Q6_K | 6 | 5.7 GB | High | C50 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | C52 |
F16 | 16 | 14.3 GB | Maximum | F0 |
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 RX 6950 XT 16GB run Vicuna 7B?
Yes, RX 6950 XT 16GB can run Vicuna 7B with a C grade (Tight fit). Expected decode speed: 78.2 tok/s.
How much VRAM does Vicuna 7B need?
Vicuna 7B (7B parameters) requires approximately 14.6 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 RX 6950 XT 16GB?
On RX 6950 XT 16GB, Vicuna 7B achieves approximately 78.2 tokens per second decode speed with a time-to-first-token of 2474ms using Q4_K_M quantization.
Can RX 6950 XT 16GB run Vicuna 7B for coding?
For coding workloads, Vicuna 7B on RX 6950 XT 16GB receives a C grade with 78.2 tok/s and 4K context.
What context window can Vicuna 7B use on RX 6950 XT 16GB?
On RX 6950 XT 16GB, 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.
Embed this result▼
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<iframe src="https://willitrunai.com/embed/vicuna-7b-on-rx-6950-xt-16gb" 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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