Can InternLM 7B run on RTX A4500 20GB?
YES — Runs Great
InternLM 7B needs ~15.3 GB VRAM. RTX A4500 20GB has 20.0 GB. With Q4_K_M quantization, expect ~98 tok/s.
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
Fit status
Runs well
Decode
98.0 tok/s
TTFT
1976 ms
Safe context
8K
Memory
15.3 GB / 20.0 GB
Memory breakdown
See how fast it feels
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 | 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 |
Quantization options
How InternLM 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 | 3.9 GB | 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 |
Get started
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
More models your RTX A4500 20GB can run
| 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 | |
| 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 |
Frequently asked questions
Can RTX A4500 20GB run InternLM 7B?
Yes, RTX A4500 20GB can run InternLM 7B with a A grade (Runs well). Expected decode speed: 98.0 tok/s.
How much VRAM does InternLM 7B need?
InternLM 7B (7B parameters) requires approximately 15.3 GB of memory with Q4_K_M quantization.
What is the best quantization for InternLM 7B?
The recommended quantization for InternLM 7B is Q4_K_M, which balances quality and memory efficiency.
What speed will InternLM 7B run at on RTX A4500 20GB?
On RTX A4500 20GB, InternLM 7B achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.
Can RTX A4500 20GB run InternLM 7B for coding?
For coding workloads, InternLM 7B on RTX A4500 20GB receives a A grade with 98.0 tok/s and 8K context.
What context window can InternLM 7B use on RTX A4500 20GB?
On RTX A4500 20GB, 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.
Embed this result▼
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/internlm-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: