RTX 5090 32GB经济之选32 GB VRAM (+8)1792 GB/s (+1120)
C
Raises estimated decode speed by about 159%.98.4 tok/s 解码
Raises estimated decode speed by about 159%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Can internlm2 5 20b chat run on Quadro RTX 6000 24GB?
YES — Runs Great
C54Usable
Estimated from fit model
internlm2 5 20b chat needs ~18.1 GB VRAM. Quadro RTX 6000 24GB has 24.0 GB. With Q4_K_M quantization, expect ~38 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: Balanced
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
Q4_K_M (Medium quality) — 18.1 GB, 38.0 tok/s, Runs well
18.1 GB required24.0 GB available
75% VRAM used
Fit status
Runs well
Decode
38.0 tok/s
TTFT
5094 ms
Safe context
56K
Memory
18.1 GB / 24.0 GB
Memory breakdown
Weights12.2 GB
KV Cache2.3 GB
Runtime1.2 GB
Headroom2.4 GB
See how fast it feels
See how fast it feelsinternlm2 5 20b chat on Quadro RTX 6000 24GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 38.0 tok/s decode · 5.1s TTFT (warm) · 95 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 38.0 tok/s | 2778 ms | 56K |
| Coding | C | Runs well | 38.0 tok/s | 5094 ms | 56K |
| Agentic Coding | C | Tight fit | 38.0 tok/s | 7409 ms | 56K |
| Reasoning | C | Runs well | 38.0 tok/s | 6020 ms | 56K |
| RAG | C | Tight fit | 38.0 tok/s | 9262 ms | 56K |
Quantization options
How internlm2 5 20b chat (20B params) fits at each quantization level on Quadro RTX 6000 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 7.8 GB | Low | C47 |
Q3_K_S | 3 | 9.8 GB | Low | C48 |
NVFP4 | 4 | 11.2 GB | Medium | C49 |
Q4_K_M | 4 | 12.2 GB | Medium | C50 |
Q5_K_M | 5 | 14.4 GB | High | C50 |
Q6_KBest for your GPU | 6 | 16.4 GB | High | C49 |
Q8_0 | 8 | 21.4 GB | Very High | F0 |
F16 | 16 | 41.0 GB | Maximum | F0 |
Get started
Copy-paste commands to run internlm2 5 20b chat on your machine.
Run
lms load hf-bartowski--internlm2-5-20b-chat-gguf && lms server start升级选项
能流畅运行 internlm2 5 20b chat 的硬件
Frequently asked questions
Can Quadro RTX 6000 24GB run internlm2 5 20b chat?
Yes, Quadro RTX 6000 24GB can run internlm2 5 20b chat with a C grade (Runs well). Expected decode speed: 38.0 tok/s.
How much VRAM does internlm2 5 20b chat need?
internlm2 5 20b chat (20B parameters) requires approximately 18.1 GB of memory with Q4_K_M quantization.
What is the best quantization for internlm2 5 20b chat?
The recommended quantization for internlm2 5 20b chat is Q4_K_M, which balances quality and memory efficiency.
What speed will internlm2 5 20b chat run at on Quadro RTX 6000 24GB?
On Quadro RTX 6000 24GB, internlm2 5 20b chat achieves approximately 38.0 tokens per second decode speed with a time-to-first-token of 5094ms using Q4_K_M quantization.
Can Quadro RTX 6000 24GB run internlm2 5 20b chat for coding?
For coding workloads, internlm2 5 20b chat on Quadro RTX 6000 24GB receives a C grade with 38.0 tok/s and 56K context.
What context window can internlm2 5 20b chat use on Quadro RTX 6000 24GB?
On Quadro RTX 6000 24GB, internlm2 5 20b chat can safely use up to 56K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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<iframe src="https://willitrunai.com/embed/hf-bartowski--internlm2-5-20b-chat-gguf-on-quadro-rtx-6000-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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