Would CUDA be a better path than Intel Arc A580 8GB for MD Judge v0 2 internlm2 7b 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 MD Judge v0 2 internlm2 7b i1 run on Intel Arc A580 8GB?

Q: What should I upgrade first if MD Judge v0 2 internlm2 7b i1 feels slow on Intel Arc A580 8GB?

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.

YES — Tight Fit

C52Usable

Estimated from fit model

MD Judge v0 2 internlm2 7b i1 needs ~6.8 GB VRAM. Intel Arc A580 8GB has 8.0 GB. With Q4_K_M quantization, expect ~59 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: MediumStack: StandardBottleneck: Balanced

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) — 6.8 GB, 58.8 tok/s, Tight fit

6.8 GB required8.0 GB available

85% VRAM used

Fit status

Tight fit

Decode

58.8 tok/s

TTFT

3295 ms

Safe context

40K

Memory

6.8 GB / 8.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsMD Judge v0 2 internlm2 7b i1 on Intel Arc A580 8GB
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: 58.8 tok/s decode · 3.3s TTFT (warm) · 147 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

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

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.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	58.8 tok/s	1797 ms	40K
Coding	C	Tight fit	58.8 tok/s	3295 ms	40K
Agentic Coding	C	Runs with offload	58.8 tok/s	4793 ms	40K
Reasoning	C	Tight fit	58.8 tok/s	3894 ms	40K
RAG	C	Runs with offload	58.8 tok/s	5991 ms	40K

Quantization options

How MD Judge v0 2 internlm2 7b i1 (7B params) fits at each quantization level on Intel Arc A580 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C53
Q3_K_S	3	3.4 GB	Low	C53
NVFP4	4

Get started

Copy-paste commands to run MD Judge v0 2 internlm2 7b i1 on your machine.

Run

lms load hf-mradermacher--md-judge-v0-2-internlm2-7b-i1-gguf && lms server start

Upgrade options

Hardware that runs MD Judge v0 2 internlm2 7b i1 well

Intel Arc B570 10GBBudget pick

10 GB VRAM (+2)

Adds memory headroom for longer context windows and future model growth.48.1 tok/s decode

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

~$219 MSRP

Intel Arc B580 12GBBest value

12 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.51.3 tok/s decode

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

~$249 MSRP

Frequently asked questions

Can Intel Arc A580 8GB run MD Judge v0 2 internlm2 7b i1?

Yes, Intel Arc A580 8GB can run MD Judge v0 2 internlm2 7b i1 with a C grade (Tight fit). Expected decode speed: 58.8 tok/s.

How much VRAM does MD Judge v0 2 internlm2 7b i1 need?

MD Judge v0 2 internlm2 7b i1 (7B parameters) requires approximately 6.8 GB of memory with Q4_K_M quantization.

What is the best quantization for MD Judge v0 2 internlm2 7b i1?

The recommended quantization for MD Judge v0 2 internlm2 7b i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will MD Judge v0 2 internlm2 7b i1 run at on Intel Arc A580 8GB?

On Intel Arc A580 8GB, MD Judge v0 2 internlm2 7b i1 achieves approximately 58.8 tokens per second decode speed with a time-to-first-token of 3295ms using Q4_K_M quantization.

Can Intel Arc A580 8GB run MD Judge v0 2 internlm2 7b i1 for coding?

For coding workloads, MD Judge v0 2 internlm2 7b i1 on Intel Arc A580 8GB receives a C grade with 58.8 tok/s and 40K context.

What context window can MD Judge v0 2 internlm2 7b i1 use on Intel Arc A580 8GB?

On Intel Arc A580 8GB, MD Judge v0 2 internlm2 7b i1 can safely use up to 40K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Intel Arc A580 8GB See all hardware for MD Judge v0 2 internlm2 7b i1

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