Can Qwen3.5 27B run on Intel Arc Pro B60 24GB?

YES — With Offload

C48Usable

Estimated from fit model

Qwen3.5 27B needs ~22.9 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~15 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: 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) — 22.9 GB, 15.0 tok/s, Runs with offload

22.9 GB required24.0 GB available

95% VRAM used

Fit status

Runs with offload

Decode

15.0 tok/s

TTFT

12949 ms

Safe context

21K

Memory

22.9 GB / 24.0 GB

Memory breakdown

Weights16.5 GB

KV Cache3.2 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsQwen3.5 27B on Intel Arc Pro B60 24GB
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: 15.0 tok/s decode · 12.9s TTFT (warm) · 37 tok/s prefill

What limits this setup

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

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

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	Tight fit	15.0 tok/s	7063 ms	21K
Coding	C	Runs with offload	15.0 tok/s	12949 ms	21K
Agentic Coding	D	Very compromised (needs ~1.3 GB host RAM)	9.6 tok/s	29223 ms	21K
Reasoning	C	Runs with offload	15.0 tok/s	15304 ms	21K
RAG	D	Very compromised (needs ~1.3 GB host RAM)	9.6 tok/s	36529 ms	21K

Quantization options

How Qwen3.5 27B (27B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	C50
Q3_K_S	3	13.2 GB	Low	C50
NVFP4	4	15.1 GB	Medium	C50
Q4_K_MBest for your GPU	4	16.5 GB	Medium	C50
Q5_K_M	5	19.4 GB	High	F0
Q6_K	6	22.1 GB	High	F0
Q8_0	8	28.9 GB	Very High	F0
F16	16	55.4 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen3.5 27B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "unsloth/Qwen3.5-27B-GGUF" \
  --hf-file "Qwen3.5-27B-GGUF-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Opciones de mejora

Hardware que ejecuta bien Qwen3.5 27B

MacBook Pro M4 Pro 64GBOpción económica

64 GB Unified (+40)

Sube la velocidad estimada de decodificación alrededor de un 41%.21.1 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 41%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$1,599 MSRP

Radeon AI PRO R9700 32GBMejor relación calidad-precio

32 GB VRAM (+8)640 GB/s (+184)

Sube la velocidad estimada de decodificación alrededor de un 53%.22.9 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 53%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$1,899 MSRP

Frequently asked questions

Can Intel Arc Pro B60 24GB run Qwen3.5 27B?

Yes, Intel Arc Pro B60 24GB can run Qwen3.5 27B with a C grade (Runs with offload). Expected decode speed: 15.0 tok/s.

How much VRAM does Qwen3.5 27B need?

Qwen3.5 27B (27B parameters) requires approximately 22.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen3.5 27B?

The recommended quantization for Qwen3.5 27B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen3.5 27B run at on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Qwen3.5 27B achieves approximately 15.0 tokens per second decode speed with a time-to-first-token of 12949ms using Q4_K_M quantization.

Can Intel Arc Pro B60 24GB run Qwen3.5 27B for coding?

For coding workloads, Qwen3.5 27B on Intel Arc Pro B60 24GB receives a C grade with 15.0 tok/s and 21K context.

What context window can Qwen3.5 27B use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Qwen3.5 27B can safely use up to 21K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Qwen3.5 27B feels slow on Intel Arc Pro B60 24GB?

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.

Would CUDA be a better path than Intel Arc Pro B60 24GB for Qwen3.5 27B?

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 Pro B60 24GB See all hardware for Qwen3.5 27B

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