Would CUDA be a better path than Intel Arc A770 16GB for internlm2 math plus 20b i1?

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.

Can internlm2 math plus 20b i1 run on Intel Arc A770 16GB?

Q: What should I upgrade first if internlm2 math plus 20b i1 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.

YES — With Offload

D37Poor

Estimated from fit model

internlm2 math plus 20b i1 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

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

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 math plus 20b i1 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs with offload	20.7 tok/s	5112 ms	9K
Coding	D	Runs with offload (needs ~0.7 GB host RAM)	13.6 tok/s	14275 ms	9K
Agentic Coding	F	Too heavy	10.3 tok/s	27233 ms	9K
Reasoning	D	Runs with offload (needs ~0.7 GB host RAM)	13.6 tok/s	16870 ms	9K
RAG	F	Too heavy	10.3 tok/s	34041 ms

Quantization options

How internlm2 math plus 20b i1 (20B params) fits at each quantization level on Intel Arc A770 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.8 GB	Low	C51
Q3_K_S	3	9.8 GB	Low	C51
NVFP4	4

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

Intel Arc Pro B60 24GBBudget pick

24 GB VRAM (+8)

Removes host-memory offload, which is usually the single biggest latency and throughput win.20.2 tok/s decode

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

Raises estimated decode speed by about 49%.

~$599 MSRP

Intel Data Center GPU Max 1550 128GBBest value

128 GB VRAM (+112)3200 GB/s (+2640)

Removes host-memory offload, which is usually the single biggest latency and throughput win.165.2 tok/s decode

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

Raises estimated decode speed by about 1115%.

~$15,000 MSRP

Gaudi 3 128GBIntel upgrade

128 GB VRAM (+112)3700 GB/s (+3140)

Removes host-memory offload, which is usually the single biggest latency and throughput win.212.3 tok/s decode

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

Raises estimated decode speed by about 1461%.

~$15,000 MSRP

Frequently asked questions

Can Intel Arc A770 16GB run internlm2 math plus 20b i1?

Yes, Intel Arc A770 16GB can run internlm2 math plus 20b i1 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 math plus 20b i1 need?

internlm2 math plus 20b i1 (20B parameters) requires approximately 17.0 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 Intel Arc A770 16GB?

On Intel Arc A770 16GB, internlm2 math plus 20b i1 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 math plus 20b i1 for coding?

For coding workloads, internlm2 math plus 20b i1 on Intel Arc A770 16GB receives a D grade with 13.6 tok/s and 9K context.

What context window can internlm2 math plus 20b i1 use on Intel Arc A770 16GB?

On Intel Arc A770 16GB, internlm2 math plus 20b i1 can safely use up to 9K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Intel Arc A770 16GB See all hardware for internlm2 math plus 20b i1

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