Will It Run AI

Can internlm2 5 20b chat run on Intel Arc A770 16GB?

YES — With Offload

D37Poor
Estimated from fit model

internlm2 5 20b chat needs ~17.0 GB VRAM. Intel Arc A770 16GB has 16.0 GB. With Q4_K_M quantization, expect ~14 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: 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) — 17.0 GB, 13.6 tok/s, Runs with offload (needs ~0.7 GB host RAM)
17.0 GB required16.0 GB available
106% VRAM needed

1.0 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.7 GB host RAM)

Decode

13.6 tok/s

TTFT

14275 ms

Safe context

9K

Memory

17.0 GB / 16.0 GB

Offload

10%

Memory breakdown

Weights12.2 GB
KV Cache2.3 GB
Runtime0.9 GB
Headroom1.6 GB

See how fast it feels

See how fast it feelsinternlm2 5 20b chat on Intel Arc A770 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: 13.6 tok/s decode · 14.3s TTFT (warm) · 34 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.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

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.

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade 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
ChatCRuns with offload20.7 tok/s5112 ms9K
CodingDRuns with offload (needs ~0.7 GB host RAM)13.6 tok/s14275 ms9K
Agentic CodingFToo heavy10.3 tok/s27233 ms9K
ReasoningDRuns with offload (needs ~0.7 GB host RAM)13.6 tok/s16870 ms9K
RAGFToo heavy10.3 tok/s34041 ms9K

Quantization options

How internlm2 5 20b chat (20B params) fits at each quantization level on Intel Arc A770 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

Opciones de mejora

Hardware que ejecuta bien internlm2 5 20b chat

IntelIntel Arc Pro B60 24GBOpción económica
24 GB VRAM (+8)
C
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.20.2 tok/s decodificación

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

Sube la velocidad estimada de decodificación alrededor de un 49%.

~$599 MSRP

IntelIntel Data Center GPU Max 1550 128GBMejor relación calidad-precio
128 GB VRAM (+112)3200 GB/s (+2640)
C
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.165.2 tok/s decodificación

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

Sube la velocidad estimada de decodificación alrededor de un 1115%.

~$15,000 MSRP

IntelGaudi 3 128GBMejora Intel
128 GB VRAM (+112)3700 GB/s (+3140)
C
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.212.3 tok/s decodificación

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

Sube la velocidad estimada de decodificación alrededor de un 1461%.

~$15,000 MSRP

Frequently asked questions

Can Intel Arc A770 16GB run internlm2 5 20b chat?

Yes, Intel Arc A770 16GB can run internlm2 5 20b chat with a D grade (Runs with offload (needs ~0.7 GB host RAM)). Expected decode speed: 13.6 tok/s.

How much VRAM does internlm2 5 20b chat need?

internlm2 5 20b chat (20B parameters) requires approximately 17.0 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 Intel Arc A770 16GB?

On Intel Arc A770 16GB, internlm2 5 20b chat achieves approximately 13.6 tokens per second decode speed with a time-to-first-token of 14275ms using Q4_K_M quantization.

Can Intel Arc A770 16GB run internlm2 5 20b chat for coding?

For coding workloads, internlm2 5 20b chat on Intel Arc A770 16GB receives a D grade with 13.6 tok/s and 9K context.

What context window can internlm2 5 20b chat use on Intel Arc A770 16GB?

On Intel Arc A770 16GB, internlm2 5 20b chat can safely use up to 9K 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 Intel Arc A770 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.

Would CUDA be a better path than Intel Arc A770 16GB for internlm2 5 20b chat?

Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.

See all results for Intel Arc A770 16GBSee all hardware for internlm2 5 20b chat
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