Would CUDA be a better path than Intel Arc A370M 4GB for TinyLlama 1.1B Chat v1.0?

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 TinyLlama 1.1B Chat v1.0 run on Intel Arc A370M 4GB?

Q: What should I upgrade first if TinyLlama 1.1B Chat v1.0 feels slow on Intel Arc A370M 4GB?

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 — Runs Great

C48Usable

Estimated from fit model

TinyLlama 1.1B Chat v1.0 needs ~2.1 GB VRAM. Intel Arc A370M 4GB has 4.0 GB. With Q4_K_M quantization, expect ~15 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: 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

Q4_K_M (Medium quality) — 2.1 GB, 15.4 tok/s, Runs well

2.1 GB required4.0 GB available

53% VRAM used

Fit status

Runs well

Decode

15.4 tok/s

TTFT

12571 ms

Safe context

252K

Memory

2.1 GB / 4.0 GB

Memory breakdown

Weights0.7 GB

KV Cache0.1 GB

Runtime0.9 GB

Headroom0.4 GB

See how fast it feels

See how fast it feelsTinyLlama 1.1B Chat v1.0 on Intel Arc A370M 4GB
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: 15.4 tok/s decode · 12.6s TTFT (warm) · 39 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	15.4 tok/s	6857 ms	162K
Coding	C	Runs well	15.4 tok/s	12571 ms	252K
Agentic Coding	C	Runs well	15.4 tok/s	18286 ms	252K
Reasoning	C	Runs well	15.4 tok/s	14857 ms	252K
RAG	C	Runs well	15.4 tok/s	22857 ms	252K

Inference speed

TinyLlama 1.1B Chat v1.0 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for TinyLlama 1.1B Chat v1.0 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~21 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	20.9	Fits
RTX 4090 24GB	24 GB	Q4_K_M	17.6	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	17.6

Quantization options

How TinyLlama 1.1B Chat v1.0 (1.100000023841858B params) fits at each quantization level on Intel Arc A370M 4GB (4.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.4 GB	Low	B56
Q3_K_S	3	0.5 GB	Low	B56
NVFP4	4

Get started

Copy-paste commands to run TinyLlama 1.1B Chat v1.0 on your machine.

Run

lms load hf-thebloke--tinyllama-1-1b-chat-v1-0-gguf && lms server start

Frequently asked questions

Can Intel Arc A370M 4GB run TinyLlama 1.1B Chat v1.0?

Yes, Intel Arc A370M 4GB can run TinyLlama 1.1B Chat v1.0 with a C grade (Runs well). Expected decode speed: 15.4 tok/s.

How much VRAM does TinyLlama 1.1B Chat v1.0 need?

TinyLlama 1.1B Chat v1.0 (1.100000023841858B parameters) requires approximately 2.1 GB of memory with Q4_K_M quantization.

What is the best quantization for TinyLlama 1.1B Chat v1.0?

The recommended quantization for TinyLlama 1.1B Chat v1.0 is Q4_K_M, which balances quality and memory efficiency.

What speed will TinyLlama 1.1B Chat v1.0 run at on Intel Arc A370M 4GB?

On Intel Arc A370M 4GB, TinyLlama 1.1B Chat v1.0 achieves approximately 15.4 tokens per second decode speed with a time-to-first-token of 12571ms using Q4_K_M quantization.

Can Intel Arc A370M 4GB run TinyLlama 1.1B Chat v1.0 for coding?

For coding workloads, TinyLlama 1.1B Chat v1.0 on Intel Arc A370M 4GB receives a C grade with 15.4 tok/s and 252K context.

What context window can TinyLlama 1.1B Chat v1.0 use on Intel Arc A370M 4GB?

On Intel Arc A370M 4GB, TinyLlama 1.1B Chat v1.0 can safely use up to 252K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Intel Arc A370M 4GB See all hardware for TinyLlama 1.1B Chat v1.0

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