Can internlm2 5 20b chat run on NVIDIA H200 PCIe 141GB?

YES — Runs Great

C46Usable

Estimated from fit model

internlm2 5 20b chat needs ~29.8 GB VRAM. NVIDIA H200 PCIe 141GB has 141.0 GB. With Q4_K_M quantization, expect ~280 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 29.8 GB, 280.0 tok/s, Runs well

29.8 GB required141.0 GB available

21% VRAM used

Fit status

Runs well

Decode

280.0 tok/s

TTFT

691 ms

Safe context

775K

Memory

29.8 GB / 141.0 GB

Memory breakdown

Weights12.2 GB

KV Cache2.3 GB

Runtime1.2 GB

Headroom14.1 GB

See how fast it feels

See how fast it feelsinternlm2 5 20b chat on NVIDIA H200 PCIe 141GB
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: 280.0 tok/s decode · 691ms TTFT (warm) · 700 tok/s prefill

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	C	Runs well	280.0 tok/s	377 ms	775K
Coding	C	Runs well	280.0 tok/s	691 ms	775K
Agentic Coding	C	Runs well	280.0 tok/s	1006 ms	775K
Reasoning	C	Runs well	280.0 tok/s	817 ms	775K
RAG	C	Runs well	280.0 tok/s	1257 ms	775K

Inference speed

internlm2 5 20b chat inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for internlm2 5 20b chat at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	98.4	Fits
RTX 4090 24GB	24 GB	Q4_K_M	62.8	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	56.7	Fits
RTX 3090 24GB	24 GB	Q4_K_M	53.7	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	45.6	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	38.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	36.1	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	35.6	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	35.6	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	31.7	Heavy offload
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	22.4	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	19.7	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	18.0	Fits
RTX 4070 12GB	12 GB	Q4_K_M	11.2	Too big
RTX 3060 12GB	12 GB	Q4_K_M	7.1	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.6	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How internlm2 5 20b chat (20B params) fits at each quantization level on NVIDIA H200 PCIe 141GB (141.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.8 GB	Low	D38
Q3_K_S	3	9.8 GB	Low	D38
NVFP4	4	11.2 GB	Medium	D38
Q4_K_M	4	12.2 GB	Medium	D38
Q5_K_M	5	14.4 GB	High	D38
Q6_K	6	16.4 GB	High	D38
Q8_0	8	21.4 GB	Very High	D38
F16Best for your GPU	16	41.0 GB	Maximum	C41

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

Frequently asked questions

Can NVIDIA H200 PCIe 141GB run internlm2 5 20b chat?

Yes, NVIDIA H200 PCIe 141GB can run internlm2 5 20b chat with a C grade (Runs well). Expected decode speed: 280.0 tok/s.

How much VRAM does internlm2 5 20b chat need?

internlm2 5 20b chat (20B parameters) requires approximately 29.8 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 NVIDIA H200 PCIe 141GB?

On NVIDIA H200 PCIe 141GB, internlm2 5 20b chat achieves approximately 280.0 tokens per second decode speed with a time-to-first-token of 691ms using Q4_K_M quantization.

Can NVIDIA H200 PCIe 141GB run internlm2 5 20b chat for coding?

For coding workloads, internlm2 5 20b chat on NVIDIA H200 PCIe 141GB receives a C grade with 280.0 tok/s and 775K context.

What context window can internlm2 5 20b chat use on NVIDIA H200 PCIe 141GB?

On NVIDIA H200 PCIe 141GB, internlm2 5 20b chat can safely use up to 775K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA H200 PCIe 141GB See all hardware for internlm2 5 20b chat

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