Will It Run AI

Can internlm2 5 20b chat run on RTX 5080 16GB?

BARELY — Tight on Memory

C40Usable
Estimated from fit model

internlm2 5 20b chat needs ~17.3 GB VRAM. RTX 5080 16GB has 16.0 GB. With Q4_K_M quantization, expect ~33 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: HighStack: BasicBottleneck: Host offload
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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) — 17.3 GB, 33.2 tok/s, Very compromised (needs ~0.9 GB host RAM)
17.3 GB required16.0 GB available
108% VRAM needed

1.3 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.9 GB host RAM)

Decode

33.2 tok/s

TTFT

5835 ms

Safe context

7K

Memory

17.3 GB / 16.0 GB

Offload

10%

Memory breakdown

Weights12.2 GB
KV Cache2.3 GB
Runtime1.2 GB
Headroom1.6 GB

See how fast it feels

See how fast it feelsinternlm2 5 20b chat on RTX 5080 16GB
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: 33.2 tok/s decode · 5.8s TTFT (warm) · 83 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly 0.9 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCRuns with offload (needs ~0.1 GB host RAM)38.3 tok/s2759 ms7K
CodingCVery compromised (needs ~0.9 GB host RAM)33.2 tok/s5835 ms7K
Agentic CodingFToo heavy25.6 tok/s10992 ms7K
ReasoningCVery compromised (needs ~0.9 GB host RAM)33.2 tok/s6896 ms7K
RAGFToo heavy25.6 tok/s13739 ms7K

Quantization options

How internlm2 5 20b chat (20B params) fits at each quantization level on RTX 5080 16GB (16.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
7.8 GB
LowC51
Q3_K_S
3
9.8 GB
LowC51
NVFP4
4
11.2 GB
MediumC50
Q4_K_MBest for your GPU
4
12.2 GB
MediumC50
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 5 20b chat on your machine.

Run

lms load hf-bartowski--internlm2-5-20b-chat-gguf && lms server start

アップグレードオプション

internlm2 5 20b chatを快適に動かすハードウェア

NVIDIARTX 4000 Ada 20GBコスパ重視
20 GB VRAM (+4)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.23 tok/sデコード

Removes host-memory offload, which is usually the single biggest latency and throughput win.

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

〜$1,250 MSRP

NVIDIARTX 3090 24GBコスパ最良
24 GB VRAM (+8)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.53.7 tok/sデコード

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 62%.

〜$1,499 MSRP

NVIDIARTX 4090 24GBNVIDIAアップグレード
24 GB VRAM (+8)1008 GB/s (+48)
B
Removes host-memory offload, which is usually the single biggest latency and throughput win.62.8 tok/sデコード

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 89%.

〜$1,599 MSRP

Frequently asked questions

Can RTX 5080 16GB run internlm2 5 20b chat?

Yes, RTX 5080 16GB can run internlm2 5 20b chat with a C grade (Very compromised (needs ~0.9 GB host RAM)). Expected decode speed: 33.2 tok/s.

How much VRAM does internlm2 5 20b chat need?

internlm2 5 20b chat (20B parameters) requires approximately 17.3 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 RTX 5080 16GB?

On RTX 5080 16GB, internlm2 5 20b chat achieves approximately 33.2 tokens per second decode speed with a time-to-first-token of 5835ms using Q4_K_M quantization.

Can RTX 5080 16GB run internlm2 5 20b chat for coding?

For coding workloads, internlm2 5 20b chat on RTX 5080 16GB receives a C grade with 33.2 tok/s and 7K context.

What context window can internlm2 5 20b chat use on RTX 5080 16GB?

On RTX 5080 16GB, internlm2 5 20b chat can safely use up to 7K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if internlm2 5 20b chat feels slow on RTX 5080 16GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for RTX 5080 16GBSee all hardware for internlm2 5 20b chat
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<iframe src="https://willitrunai.com/embed/hf-bartowski--internlm2-5-20b-chat-gguf-on-rtx-5080-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

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