InternLM Chat 7B needs ~15.3 GB VRAM. RTX A4500 20GB has 20.0 GB. With Q4_K_M quantization, expect ~98 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
98.0 tok/s
TTFT
1976 ms
Safe context
8K
Memory
15.3 GB / 20.0 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 98.0 tok/s | 1078 ms | 8K |
| Coding | A | Runs well | 98.0 tok/s | 1976 ms | 8K |
| Agentic Coding | B | Very compromised (needs ~0.6 GB host RAM) | 64.8 tok/s | 4348 ms | 8K |
| Reasoning | A | Runs well | 98.0 tok/s | 2335 ms | 8K |
| RAG | B | Very compromised (needs ~0.6 GB host RAM) | 64.8 tok/s | 5435 ms | 8K |
How InternLM Chat 7B (7B params) fits at each quantization level on RTX A4500 20GB (20.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B66 |
Q3_K_S | 3 | 3.4 GB | Low | B66 |
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 |
|---|---|---|---|---|
 Qwen3-Coder 30B A3B Instruct | 30.5B | A | 41.2 tok/s | |
| Qwen 3.5 27B | 27B | A | 18.6 tok/s |
Yes, RTX A4500 20GB can run InternLM Chat 7B with a A grade (Runs well). Expected decode speed: 98.0 tok/s.
InternLM Chat 7B (7B parameters) requires approximately 15.3 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 A4500 20GB, InternLM Chat 7B achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.
For coding workloads, InternLM Chat 7B on RTX A4500 20GB receives a A grade with 98.0 tok/s and 8K context.
On RTX A4500 20GB, 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-rtx-a4500-20gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| B67 |
Q4_K_M | 4 | 4.3 GB | Medium | B67 |
Q5_K_M | 5 | 5.0 GB | High | B67 |
Q6_K | 6 | 5.7 GB | High | B68 |
Q8_0 | 8 | 7.5 GB | Very High | B69 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | A71 |
| Qwen 3.6 27B | 27B | S | 23 tok/s |
| Qwen3-VL 30B A3B Instruct | 30B | A | 43.8 tok/s |
| Qwen 3.5 9B | 9B | S | 97.7 tok/s |