Will It Run AI

Can internlm2 math plus 20b i1 run on RTX 4070 12GB?

YES — With Q2_K

C50Usable
Estimated from fit model

internlm2 math plus 20b i1 needs ~12.5 GB VRAM. RTX 4070 12GB has 12.0 GB. With Q2_K quantization, expect ~28 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: Balanced
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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.

internlm2 math plus 20b i1 at Q4_K_M needs 16.9 GB — too much for RTX 4070 12GB (12.0 GB). Runs at Q2_K (12.5 GB) with low quality.
Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 16.9 GB, exceeds 12.0 GB available
16.9 GB required12.0 GB available
141% VRAM needed

4.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

11.2 tok/s

TTFT

17223 ms

Safe context

4K

Memory

16.9 GB / 12.0 GB

Offload

30%

Memory breakdown

Weights12.2 GB
KV Cache2.3 GB
Runtime1.2 GB
Headroom1.2 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsinternlm2 math plus 20b i1 on RTX 4070 12GB
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: 11.2 tok/s decode · 17.2s TTFT (warm) · 28 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy13.1 tok/s8079 ms4K
CodingFToo heavy11.2 tok/s17223 ms4K
Agentic CodingFToo heavy8.6 tok/s32906 ms4K
ReasoningFToo heavy11.2 tok/s20354 ms4K
RAGFToo heavy8.6 tok/s41132 ms4K

Quantization options

How internlm2 math plus 20b i1 (20B params) fits at each quantization level on RTX 4070 12GB (12.0 GB usable).

QuantBitsVRAMQualityFit
Q2_KBest for your GPU
2
7.8 GB
LowC51
Q3_K_S
3
9.8 GB
LowF0
NVFP4
4
11.2 GB
MediumF0
Q4_K_M
4
12.2 GB
MediumF0
Q5_K_M
5
14.4 GB
HighF0
Q6_K
6
16.4 GB
HighF0
Q8_0
8
21.4 GB
Very HighF0
F16
16
41.0 GB
MaximumF0

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

Opciones de mejora

Hardware que ejecuta bien internlm2 math plus 20b i1

NVIDIARTX 5060 Ti 16GBMejor relación calidad-precio
16 GB VRAM (+4)
D
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.14.8 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Sube la velocidad estimada de decodificación alrededor de un 32%.

~$449 MSRP

NVIDIARTX 4060 Ti 16GBMejora NVIDIA
16 GB VRAM (+4)
D
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.10.9 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$499 MSRP

NVIDIARTX 4000 Ada 20GBOpción económica
20 GB VRAM (+8)
C
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.23 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$1,250 MSRP

NVIDIARTX 4090 24GBMayor salto
24 GB VRAM (+12)1008 GB/s (+504)
B
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.62.8 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$1,599 MSRP

Frequently asked questions

Can RTX 4070 12GB run internlm2 math plus 20b i1?

Yes, RTX 4070 12GB can run internlm2 math plus 20b i1 at Q2_K quantization (Runs with offload (needs ~0.3 GB host RAM)). The recommended Q4_K_M requires 16.9 GB which exceeds available memory, but at Q2_K it needs only 12.5 GB. Expected decode speed: 28.1 tok/s.

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

internlm2 math plus 20b i1 (20B parameters) requires approximately 16.9 GB at Q4_K_M quantization. On RTX 4070 12GB, it fits at Q2_K using 12.5 GB.

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

The recommended quantization is Q4_K_M, but on RTX 4070 12GB the best fitting quantization is Q2_K, which uses 12.5 GB.

What speed will internlm2 math plus 20b i1 run at on RTX 4070 12GB?

On RTX 4070 12GB, internlm2 math plus 20b i1 achieves approximately 28.1 tokens per second decode speed with a time-to-first-token of 6879ms using Q2_K quantization.

Can RTX 4070 12GB run internlm2 math plus 20b i1 for coding?

For coding workloads, internlm2 math plus 20b i1 on RTX 4070 12GB receives a F grade with 11.2 tok/s and 4K context.

What context window can internlm2 math plus 20b i1 use on RTX 4070 12GB?

On RTX 4070 12GB, internlm2 math plus 20b i1 can safely use up to 12K tokens of context at Q2_K quantization. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if internlm2 math plus 20b i1 feels slow on RTX 4070 12GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for RTX 4070 12GBSee all hardware for internlm2 math plus 20b i1
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