Can internlm2 math plus 20b i1 run on RTX A6000 48GB?

YES — Runs Great

C48Usable

Estimated from fit model

internlm2 math plus 20b i1 needs ~20.5 GB VRAM. RTX A6000 48GB has 48.0 GB. With Q4_K_M quantization, expect ~48 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 20.5 GB, 47.8 tok/s, Runs well

20.5 GB required48.0 GB available

43% VRAM used

Fit status

Runs well

Decode

47.8 tok/s

TTFT

4047 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 math plus 20b i1 on RTX A6000 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: 47.8 tok/s decode · 4.0s TTFT (warm) · 120 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	47.8 tok/s	2207 ms	203K
Coding	C	Runs well	47.8 tok/s	4047 ms	203K
Agentic Coding	C	Runs well	47.8 tok/s	5886 ms	203K
Reasoning	C	Runs well	47.8 tok/s	4782 ms	203K
RAG	C	Runs well	47.8 tok/s	7357 ms	203K

Quantization options

How internlm2 math plus 20b i1 (20B params) fits at each quantization level on RTX A6000 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 math plus 20b i1 on your machine.

Run

lms load hf-mradermacher--internlm2-math-plus-20b-i1-gguf && lms server start

Upgrade options

Hardware that runs internlm2 math plus 20b i1 well

AMD Instinct MI210 64GBBudget pick

64 GB VRAM (+16)1638 GB/s (+870)

Raises estimated decode speed by about 91%.91.3 tok/s decode

Raises estimated decode speed by about 91%.

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

~$10,000 MSRP

Frequently asked questions

Can RTX A6000 48GB run internlm2 math plus 20b i1?

Yes, RTX A6000 48GB can run internlm2 math plus 20b i1 with a C grade (Runs well). Expected decode speed: 47.8 tok/s.

How much VRAM does internlm2 math plus 20b i1 need?

internlm2 math plus 20b i1 (20B parameters) requires approximately 20.5 GB of memory with Q4_K_M quantization.

What is the best quantization for internlm2 math plus 20b i1?

The recommended quantization for internlm2 math plus 20b i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will internlm2 math plus 20b i1 run at on RTX A6000 48GB?

On RTX A6000 48GB, internlm2 math plus 20b i1 achieves approximately 47.8 tokens per second decode speed with a time-to-first-token of 4047ms using Q4_K_M quantization.

Can RTX A6000 48GB run internlm2 math plus 20b i1 for coding?

For coding workloads, internlm2 math plus 20b i1 on RTX A6000 48GB receives a C grade with 47.8 tok/s and 203K context.

What context window can internlm2 math plus 20b i1 use on RTX A6000 48GB?

On RTX A6000 48GB, internlm2 math plus 20b i1 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 A6000 48GB See all hardware for internlm2 math plus 20b i1

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-mradermacher--internlm2-math-plus-20b-i1-gguf-on-a6000-48gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: