RX 7900 XT 20GBBudget pick
20 GB VRAM (+4)800 GB/s (+576)
B
Raises estimated decode speed by about 221%.98 tok/s decode
Raises estimated decode speed by about 221%.
Adds memory headroom for longer context windows and future model growth.
~$899 MSRP
Mistral 7B Instruct v0.3 needs ~8.7 GB VRAM. Intel Arc Pro B50 16GB has 16.0 GB. With Q4_K_M quantization, expect ~31 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
30.5 tok/s
TTFT
6357 ms
Safe context
8K
Memory
8.7 GB / 16.0 GB
The raw memory story may look fine, but the software ecosystem is still a constraint here.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 30.5 tok/s | 3468 ms | 8K |
| Coding | B | Runs well | 30.5 tok/s | 6357 ms | 8K |
| Agentic Coding | B | Runs well | 30.5 tok/s | 9247 ms | 8K |
| Reasoning | B | Runs well | 30.5 tok/s | 7513 ms | 8K |
| RAG | B | Runs well | 30.5 tok/s | 11559 ms | 8K |
How Mistral 7B Instruct v0.3 (7B params) fits at each quantization level on Intel Arc Pro B50 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B59 |
Q3_K_S | 3 | 3.4 GB | Low | B60 |
NVFP4 | 4 |
Copy-paste commands to run Mistral 7B Instruct v0.3 on your machine.
Run
lms load Mistral-7B-Instruct-v0.3 && lms server startUpgrade options
Raises estimated decode speed by about 221%.
Adds memory headroom for longer context windows and future model growth.
~$899 MSRP
Raises estimated decode speed by about 221%.
Adds memory headroom for longer context windows and future model growth.
~$999 MSRP
Yes, Intel Arc Pro B50 16GB can run Mistral 7B Instruct v0.3 with a B grade (Runs well). Expected decode speed: 30.5 tok/s.
Mistral 7B Instruct v0.3 (7B parameters) requires approximately 8.7 GB of memory with Q4_K_M quantization.
The recommended quantization for Mistral 7B Instruct v0.3 is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc Pro B50 16GB, Mistral 7B Instruct v0.3 achieves approximately 30.5 tokens per second decode speed with a time-to-first-token of 6357ms using Q4_K_M quantization.
For coding workloads, Mistral 7B Instruct v0.3 on Intel Arc Pro B50 16GB receives a B grade with 30.5 tok/s and 8K context.
On Intel Arc Pro B50 16GB, Mistral 7B Instruct v0.3 can safely use up to 8K tokens of context. The model's official context limit is 8K, 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/mistral-7b-instruct-v0.3-on-arc-pro-b50-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
3.9 GB |
| Medium |
| B60 |
Q4_K_M | 4 | 4.3 GB | Medium | B60 |
Q5_K_M | 5 | 5.0 GB | High | B61 |
Q6_K | 6 | 5.7 GB | High | B62 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | B64 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.