Can internlm2 math plus 20b i1 run on NVIDIA A10 24GB?

YES — Runs Great

C54Usable

Estimated from fit model

internlm2 math plus 20b i1 needs ~18.1 GB VRAM. NVIDIA A10 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 18.1 GB, 38.4 tok/s, Runs well

18.1 GB required24.0 GB available

75% VRAM used

Fit status

Runs well

Decode

38.4 tok/s

TTFT

5047 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 math plus 20b i1 on NVIDIA A10 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: 38.4 tok/s decode · 5.0s TTFT (warm) · 96 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	38.4 tok/s	2753 ms	56K
Coding	C	Runs well	38.4 tok/s	5047 ms	56K
Agentic Coding	C	Tight fit	38.4 tok/s	7341 ms	56K
Reasoning	C	Runs well	38.4 tok/s	5964 ms	56K
RAG	C	Tight fit	38.4 tok/s	9176 ms	56K

Inference speed

internlm2 math plus 20b i1 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for internlm2 math plus 20b i1 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 math plus 20b i1 (20B params) fits at each quantization level on NVIDIA A10 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	C49
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 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

RTX 5090 32GBBudget pick

32 GB VRAM (+8)1792 GB/s (+1192)

Raises estimated decode speed by about 156%.98.4 tok/s decode

Raises estimated decode speed by about 156%.

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

~$1,999 MSRP

Frequently asked questions

Can NVIDIA A10 24GB run internlm2 math plus 20b i1?

Yes, NVIDIA A10 24GB can run internlm2 math plus 20b i1 with a C grade (Runs well). Expected decode speed: 38.4 tok/s.

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

internlm2 math plus 20b i1 (20B parameters) requires approximately 18.1 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 NVIDIA A10 24GB?

On NVIDIA A10 24GB, internlm2 math plus 20b i1 achieves approximately 38.4 tokens per second decode speed with a time-to-first-token of 5047ms using Q4_K_M quantization.

Can NVIDIA A10 24GB run internlm2 math plus 20b i1 for coding?

For coding workloads, internlm2 math plus 20b i1 on NVIDIA A10 24GB receives a C grade with 38.4 tok/s and 56K context.

What context window can internlm2 math plus 20b i1 use on NVIDIA A10 24GB?

On NVIDIA A10 24GB, internlm2 math plus 20b i1 can safely use up to 56K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA A10 24GB See all hardware for internlm2 math plus 20b i1

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

38.4 tok/s

2753 ms

56K

Coding

Runs well

38.4 tok/s

5047 ms

56K

Agentic Coding

Tight fit

38.4 tok/s

7341 ms

56K

Reasoning

Runs well

38.4 tok/s

5964 ms

56K

RAG

Tight fit

38.4 tok/s

9176 ms

56K

internlm2 math plus 20b i1 inference speed — tokens per second by GPU & Mac

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

Quantization options

How internlm2 math plus 20b i1 (20B params) fits at each quantization level on NVIDIA A10 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	C49
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