Can internlm2 5 20b chat run on RTX 6000 Ada 48GB?

YES — Runs Great

C50Usable

Estimated from fit model

internlm2 5 20b chat needs ~20.5 GB VRAM. RTX 6000 Ada 48GB has 48.0 GB. With Q4_K_M quantization, expect ~65 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) — 20.5 GB, 64.5 tok/s, Runs well

20.5 GB required48.0 GB available

43% VRAM used

Fit status

Runs well

Decode

64.5 tok/s

TTFT

3000 ms

Safe context

203K

Memory

20.5 GB / 48.0 GB

Memory breakdown

Weights12.2 GB

KV Cache2.3 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsinternlm2 5 20b chat on RTX 6000 Ada 48GB
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: 64.5 tok/s decode · 3.0s TTFT (warm) · 161 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	64.5 tok/s	1637 ms	203K
Coding	C	Runs well	64.5 tok/s	3000 ms	203K
Agentic Coding	C	Runs well	64.5 tok/s	4364 ms	203K
Reasoning	C	Runs well	64.5 tok/s	3546 ms	203K
RAG	C	Runs well	64.5 tok/s	5455 ms	203K

Quantization options

How internlm2 5 20b chat (20B params) fits at each quantization level on RTX 6000 Ada 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.8 GB	Low	C42
Q3_K_S	3	9.8 GB	Low	C42
NVFP4	4	11.2 GB	Medium	C43
Q4_K_M	4	12.2 GB	Medium	C43
Q5_K_M	5	14.4 GB	High	C44
Q6_K	6	16.4 GB	High	C44
Q8_0	8	21.4 GB	Very High	C46
F16Best for your GPU	16	41.0 GB	Maximum	C47

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 RTX 6000 Ada 48GB run internlm2 5 20b chat?

Yes, RTX 6000 Ada 48GB can run internlm2 5 20b chat with a C grade (Runs well). Expected decode speed: 64.5 tok/s.

How much VRAM does internlm2 5 20b chat need?

internlm2 5 20b chat (20B parameters) requires approximately 20.5 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 RTX 6000 Ada 48GB?

On RTX 6000 Ada 48GB, internlm2 5 20b chat achieves approximately 64.5 tokens per second decode speed with a time-to-first-token of 3000ms using Q4_K_M quantization.

Can RTX 6000 Ada 48GB run internlm2 5 20b chat for coding?

For coding workloads, internlm2 5 20b chat on RTX 6000 Ada 48GB receives a C grade with 64.5 tok/s and 203K context.

What context window can internlm2 5 20b chat use on RTX 6000 Ada 48GB?

On RTX 6000 Ada 48GB, internlm2 5 20b chat can safely use up to 203K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 6000 Ada 48GB See all hardware for internlm2 5 20b chat

Embed this result▼

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<iframe src="https://willitrunai.com/embed/hf-bartowski--internlm2-5-20b-chat-gguf-on-rtx-6000-ada-48gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

64.5 tok/s

1637 ms

203K

Coding

Runs well

64.5 tok/s

3000 ms

203K

Agentic Coding

Runs well

64.5 tok/s

4364 ms

203K

Reasoning

Runs well

64.5 tok/s

3546 ms

203K

RAG

Runs well

64.5 tok/s

5455 ms

203K

Quantization options

How internlm2 5 20b chat (20B params) fits at each quantization level on RTX 6000 Ada 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.8 GB	Low	C42
Q3_K_S	3	9.8 GB	Low	C42
NVFP4	4	11.2 GB	Medium	C43
Q4_K_M	4	12.2 GB	Medium	C43
Q5_K_M	5	14.4 GB	High	C44
Q6_K	6	16.4 GB	High	C44
Q8_0	8	21.4 GB	Very High	C46
F16Best for your GPU	16	41.0 GB	Maximum	C47

Frequently asked questions

Can RTX 6000 Ada 48GB run internlm2 5 20b chat?

Yes, RTX 6000 Ada 48GB can run internlm2 5 20b chat with a C grade (Runs well). Expected decode speed: 64.5 tok/s.

How much VRAM does internlm2 5 20b chat need?

internlm2 5 20b chat (20B parameters) requires approximately 20.5 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 RTX 6000 Ada 48GB?

On RTX 6000 Ada 48GB, internlm2 5 20b chat achieves approximately 64.5 tokens per second decode speed with a time-to-first-token of 3000ms using Q4_K_M quantization.

Can RTX 6000 Ada 48GB run internlm2 5 20b chat for coding?

For coding workloads, internlm2 5 20b chat on RTX 6000 Ada 48GB receives a C grade with 64.5 tok/s and 203K context.

What context window can internlm2 5 20b chat use on RTX 6000 Ada 48GB?

On RTX 6000 Ada 48GB, internlm2 5 20b chat can safely use up to 203K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.