Would CUDA be a better path than Intel Arc B570 10GB for All MiniLM L6 v2?

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 All MiniLM L6 v2 run on Intel Arc B570 10GB?

Q: What should I upgrade first if All MiniLM L6 v2 feels slow on Intel Arc B570 10GB?

Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

YES — Runs Great

B63Good

Estimated from fit model

All MiniLM L6 v2 needs ~2.5 GB VRAM. Intel Arc B570 10GB has 10.0 GB. With F16 quantization, expect ~2 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: Memory bandwidth

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

F16 (Maximum quality) — 2.5 GB, 2.0 tok/s, Runs well

2.5 GB required10.0 GB available

25% VRAM used

Fit status

Runs well

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

256

Memory

2.5 GB / 10.0 GB

Memory breakdown

Weights0.0 GB

KV Cache0.3 GB

Runtime1.2 GB

Headroom1.0 GB

See how fast it feels

See how fast it feelsAll MiniLM L6 v2 on Intel Arc B570 10GB
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: 2.0 tok/s decode · 96.8s TTFT (warm) · 5 tok/s prefill

What limits this setup

This model fits, but memory bandwidth is the part holding decode speed back.

Throughput will feel slow

Estimated decode speed is only 2.0 tok/s, so this is more of a technical fit than a comfortable daily-driver setup.

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

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

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	B	Runs well	2.0 tok/s	52800 ms	256
Coding	B	Runs well	2.0 tok/s	96800 ms	256
Agentic Coding	B	Runs well	2.0 tok/s	140800 ms	256
Reasoning	B	Runs well	2.0 tok/s	114400 ms	256
RAG	B	Runs well	2.0 tok/s	176000 ms	256

Quantization options

How All MiniLM L6 v2 (0.023000000044703484B params) fits at each quantization level on Intel Arc B570 10GB (10.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.0 GB	Low	A74
Q3_K_S	3	0.0 GB	Low	A74
NVFP4	4

Get started

Copy-paste commands to run All MiniLM L6 v2 on your machine.

Run

ollama run all-minilm

Upgrade options

Hardware that runs All MiniLM L6 v2 well

MacBook Pro M4 16GBBudget pick

16 GB Unified (+6)

The model fits in shared memory, but shared-memory bandwidth is now the real limiter.2 tok/s decode

~$599 MSRP

MacBook Air M1 16GBBest value

16 GB Unified (+6)

The model fits in shared memory, but shared-memory bandwidth is now the real limiter.2 tok/s decode

~$999 MSRP

Frequently asked questions

Can Intel Arc B570 10GB run All MiniLM L6 v2?

Yes, Intel Arc B570 10GB can run All MiniLM L6 v2 with a B grade (Runs well). Expected decode speed: 2.0 tok/s.

How much VRAM does All MiniLM L6 v2 need?

All MiniLM L6 v2 (0.023000000044703484B parameters) requires approximately 2.5 GB of memory with F16 quantization.

What is the best quantization for All MiniLM L6 v2?

The recommended quantization for All MiniLM L6 v2 is F16, which balances quality and memory efficiency.

What speed will All MiniLM L6 v2 run at on Intel Arc B570 10GB?

On Intel Arc B570 10GB, All MiniLM L6 v2 achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using F16 quantization.

Can Intel Arc B570 10GB run All MiniLM L6 v2 for coding?

For coding workloads, All MiniLM L6 v2 on Intel Arc B570 10GB receives a B grade with 2.0 tok/s and 256 context.

What context window can All MiniLM L6 v2 use on Intel Arc B570 10GB?

On Intel Arc B570 10GB, All MiniLM L6 v2 can safely use up to 256 tokens of context. The model's official context limit is 256, but available memory constrains the safe maximum.

What should I upgrade first if All MiniLM L6 v2 feels slow on Intel Arc B570 10GB?

See all results for Intel Arc B570 10GB See all hardware for All MiniLM L6 v2

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