Will It Run AI

Can internlm2 math plus 20b i1 run on Quadro RTX 8000 48GB?

YES — Runs Great

C48Usable
Estimated from fit model

internlm2 math plus 20b i1 needs ~20.5 GB VRAM. Quadro RTX 8000 48GB has 48.0 GB. With Q4_K_M quantization, expect ~38 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 20.5 GB, 38.0 tok/s, Runs well
20.5 GB required48.0 GB available
43% VRAM used

Fit status

Runs well

Decode

38.0 tok/s

TTFT

5094 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 Quadro RTX 8000 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: 38.0 tok/s decode · 5.1s TTFT (warm) · 95 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well38.0 tok/s2778 ms203K
CodingCRuns well38.0 tok/s5094 ms203K
Agentic CodingCRuns well38.0 tok/s7409 ms203K
ReasoningCRuns well38.0 tok/s6020 ms203K
RAGCRuns well38.0 tok/s9262 ms203K

Quantization options

How internlm2 math plus 20b i1 (20B params) fits at each quantization level on Quadro RTX 8000 48GB (48.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
7.8 GB
LowC42
Q3_K_S
3
9.8 GB
LowC42
NVFP4
4
11.2 GB
MediumC43
Q4_K_M
4
12.2 GB
MediumC43
Q5_K_M
5
14.4 GB
HighC44
Q6_K
6
16.4 GB
HighC44
Q8_0
8
21.4 GB
Very HighC46
F16Best for your GPU
16
41.0 GB
MaximumC47

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-Optionen

Hardware, die internlm2 math plus 20b i1 gut ausführt

NVIDIANVIDIA A100 80GBGünstige Wahl
80 GB VRAM (+32)2039 GB/s (+1367)
C
Raises estimated decode speed by about 269%.140.4 tok/s Dekodierung

Raises estimated decode speed by about 269%.

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

ca. $15,000 MSRP

NVIDIANVIDIA A800 80GBBestes Preis-Leistungs-Verhältnis
80 GB VRAM (+32)1935 GB/s (+1263)
C
Raises estimated decode speed by about 226%.123.7 tok/s Dekodierung

Raises estimated decode speed by about 226%.

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

ca. $15,000 MSRP

NVIDIANVIDIA H800 80GBNVIDIA-Upgrade
80 GB VRAM (+32)3000 GB/s (+2328)
C
Raises estimated decode speed by about 424%.199.2 tok/s Dekodierung

Raises estimated decode speed by about 424%.

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

ca. $30,000 MSRP

Frequently asked questions

Can Quadro RTX 8000 48GB run internlm2 math plus 20b i1?

Yes, Quadro RTX 8000 48GB can run internlm2 math plus 20b i1 with a C grade (Runs well). Expected decode speed: 38.0 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 Quadro RTX 8000 48GB?

On Quadro RTX 8000 48GB, internlm2 math plus 20b i1 achieves approximately 38.0 tokens per second decode speed with a time-to-first-token of 5094ms using Q4_K_M quantization.

Can Quadro RTX 8000 48GB run internlm2 math plus 20b i1 for coding?

For coding workloads, internlm2 math plus 20b i1 on Quadro RTX 8000 48GB receives a C grade with 38.0 tok/s and 203K context.

What context window can internlm2 math plus 20b i1 use on Quadro RTX 8000 48GB?

On Quadro RTX 8000 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 Quadro RTX 8000 48GBSee all hardware for internlm2 math plus 20b i1
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