Can internlm2 5 20b chat run on Radeon Pro W7900 48GB?

YES — Runs Great

C48Usable

Estimated from fit model

internlm2 5 20b chat needs ~20.2 GB VRAM. Radeon Pro W7900 48GB has 48.0 GB. With Q4_K_M quantization, expect ~42 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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) — 20.2 GB, 41.8 tok/s, Runs well

20.2 GB required48.0 GB available

42% VRAM used

Fit status

Runs well

Decode

41.8 tok/s

TTFT

4633 ms

Safe context

205K

Memory

20.2 GB / 48.0 GB

Memory breakdown

Weights12.2 GB

KV Cache2.3 GB

Runtime0.9 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsinternlm2 5 20b chat on Radeon Pro W7900 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: 41.8 tok/s decode · 4.6s TTFT (warm) · 105 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	41.8 tok/s	2527 ms	205K
Coding	C	Runs well	41.8 tok/s	4633 ms	205K
Agentic Coding	C	Runs well	41.8 tok/s	6739 ms	205K
Reasoning	C	Runs well	41.8 tok/s	5476 ms	205K
RAG	C	Runs well	41.8 tok/s	8424 ms	205K

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

Quantization options

How internlm2 5 20b chat (20B params) fits at each quantization level on Radeon Pro W7900 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

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

Upgrade options

Hardware that runs internlm2 5 20b chat well

AMD Instinct MI210 64GBBudget pick

64 GB VRAM (+16)1638 GB/s (+774)

Raises estimated decode speed by about 118%.91.3 tok/s decode

Raises estimated decode speed by about 118%.

Adds memory headroom for longer context windows and future model growth.

~$10,000 MSRP

Frequently asked questions

Can Radeon Pro W7900 48GB run internlm2 5 20b chat?

Yes, Radeon Pro W7900 48GB can run internlm2 5 20b chat with a C grade (Runs well). Expected decode speed: 41.8 tok/s.

How much VRAM does internlm2 5 20b chat need?

internlm2 5 20b chat (20B parameters) requires approximately 20.2 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 Radeon Pro W7900 48GB?

On Radeon Pro W7900 48GB, internlm2 5 20b chat achieves approximately 41.8 tokens per second decode speed with a time-to-first-token of 4633ms using Q4_K_M quantization.

Can Radeon Pro W7900 48GB run internlm2 5 20b chat for coding?

For coding workloads, internlm2 5 20b chat on Radeon Pro W7900 48GB receives a C grade with 41.8 tok/s and 205K context.

What context window can internlm2 5 20b chat use on Radeon Pro W7900 48GB?

On Radeon Pro W7900 48GB, internlm2 5 20b chat can safely use up to 205K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Radeon Pro W7900 48GB See all hardware for internlm2 5 20b chat

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