InternLM 7B needs ~14.6 GB VRAM. Intel Arc Pro B50 16GB has 16.0 GB. With Q4_K_M quantization, expect ~28 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
Tight fit
Decode
28.3 tok/s
TTFT
6834 ms
Safe context
8K
Memory
14.6 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 | A | Runs well | 28.3 tok/s | 3728 ms | 8K |
| Coding | A | Tight fit | 28.3 tok/s | 6834 ms | 8K |
| Agentic Coding | F | Too heavy | 10.9 tok/s | 25812 ms | 8K |
| Reasoning | A | Tight fit | 28.3 tok/s | 8077 ms | 8K |
| RAG | F | Too heavy | 10.9 tok/s | 32265 ms | 8K |
How InternLM 7B (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 | B67 |
Q3_K_S | 3 | 3.4 GB | Low | B68 |
NVFP4 | 4 |
Copy-paste commands to run InternLM 7B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "InternLM/InternLM-7B" \
--hf-file "InternLM-7B-Q4_K_M.gguf" \
-c 4096 -ngl 99Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3.5 9B | 9B | S | 23.7 tok/s | |
| Qwen 3 14B | 14B | S | 15.3 tok/s |
Yes, Intel Arc Pro B50 16GB can run InternLM 7B with a A grade (Tight fit). Expected decode speed: 28.3 tok/s.
InternLM 7B (7B parameters) requires approximately 14.6 GB of memory with Q4_K_M quantization.
The recommended quantization for InternLM 7B is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc Pro B50 16GB, InternLM 7B achieves approximately 28.3 tokens per second decode speed with a time-to-first-token of 6834ms using Q4_K_M quantization.
For coding workloads, InternLM 7B on Intel Arc Pro B50 16GB receives a A grade with 28.3 tok/s and 8K context.
On Intel Arc Pro B50 16GB, InternLM 7B 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/internlm-7b-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 |
| B68 |
Q4_K_M | 4 | 4.3 GB | Medium | B69 |
Q5_K_M | 5 | 5.0 GB | High | B69 |
Q6_K | 6 | 5.7 GB | High | A70 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | A72 |
F16 | 16 | 14.3 GB | Maximum | F0 |
| Qwen 3 8B | 8B | S | 26.6 tok/s |
 Phi-4-reasoning-plus 14B | 14.7B | S | 14.5 tok/s |
 GPT-OSS 20B | 21B | A | 14.4 tok/s |
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.