InternLM Chat 7B needs ~14.9 GB VRAM. RTX A4000 16GB has 16.0 GB. With Q4_K_M quantization, expect ~73 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
73.4 tok/s
TTFT
2636 ms
Safe context
8K
Memory
14.9 GB / 16.0 GB
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 73.4 tok/s | 1438 ms | 8K |
| Coding | A | Tight fit | 73.4 tok/s | 2636 ms | 8K |
| Agentic Coding | F | Too heavy | 26.4 tok/s | 10670 ms | 8K |
| Reasoning | A | Tight fit | 73.4 tok/s | 3115 ms | 8K |
| RAG | F | Too heavy | 26.4 tok/s | 13338 ms | 8K |
How InternLM Chat 7B (7B params) fits at each quantization level on RTX A4000 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 Chat 7B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "InternLM/InternLM-Chat-7B" \
--hf-file "InternLM-Chat-7B-Q4_K_M.gguf" \
-c 4096 -ngl 99Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3.5 9B | 9B | S | 61.4 tok/s | |
| Qwen 3 14B | 14B | S | 39.7 tok/s |
Yes, RTX A4000 16GB can run InternLM Chat 7B with a A grade (Tight fit). Expected decode speed: 73.4 tok/s.
InternLM Chat 7B (7B parameters) requires approximately 14.9 GB of memory with Q4_K_M quantization.
The recommended quantization for InternLM Chat 7B is Q4_K_M, which balances quality and memory efficiency.
On RTX A4000 16GB, InternLM Chat 7B achieves approximately 73.4 tokens per second decode speed with a time-to-first-token of 2636ms using Q4_K_M quantization.
For coding workloads, InternLM Chat 7B on RTX A4000 16GB receives a A grade with 73.4 tok/s and 8K context.
On RTX A4000 16GB, InternLM Chat 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-chat-7b-on-a4000-16gb" 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 | 69.1 tok/s |
 Phi-4-reasoning-plus 14B | 14.7B | S | 37.6 tok/s |
 GPT-OSS 20B | 21B | A | 35 tok/s |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.