Can GGUF SOLARized GraniStral 14B 1902 YeAM HCT run on Intel Arc A770 16GB?

YES — Runs Great

C53Usable

Estimated from fit model

GGUF SOLARized GraniStral 14B 1902 YeAM HCT needs ~12.7 GB VRAM. Intel Arc A770 16GB has 16.0 GB. With Q4_K_M quantization, expect ~30 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 12.7 GB, 29.5 tok/s, Runs well

12.7 GB required16.0 GB available

79% VRAM used

Fit status

Runs well

Decode

29.5 tok/s

TTFT

6561 ms

Safe context

48K

Memory

12.7 GB / 16.0 GB

Memory breakdown

Weights8.5 GB

KV Cache1.6 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsGGUF SOLARized GraniStral 14B 1902 YeAM HCT 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: 29.5 tok/s decode · 6.6s TTFT (warm) · 74 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	29.5 tok/s	3579 ms	48K
Coding	C	Runs well	29.5 tok/s	6561 ms	48K
Agentic Coding	C	Tight fit	29.5 tok/s	9543 ms	48K
Reasoning	C	Runs well	29.5 tok/s	7754 ms	48K
RAG	C	Tight fit	29.5 tok/s	11929 ms	48K

Inference speed

GGUF SOLARized GraniStral 14B 1902 YeAM HCT inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for GGUF SOLARized GraniStral 14B 1902 YeAM HCT at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~141 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	140.6	Fits
RTX 4090 24GB	24 GB	Q4_K_M	89.7	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	80.9	Fits
RTX 3090 24GB	24 GB	Q4_K_M	76.7	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	75.1	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	65.2	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	54.3	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	51.5	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	35.4	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	35.4	Fits
RTX 4070 12GB	12 GB	Q4_K_M	33.2	Offloads
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	28.1	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	25.8	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	21.7	Fits
RTX 3060 12GB	12 GB	Q4_K_M	19.5	Offloads
RTX 4060 8GB	8 GB	Q4_K_M	7.2	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How GGUF SOLARized GraniStral 14B 1902 YeAM HCT (14B params) fits at each quantization level on Intel Arc A770 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	C49
Q3_K_S	3	6.9 GB	Low	C50
NVFP4	4	7.8 GB	Medium	C51
Q4_K_M	4	8.5 GB	Medium	C51
Q5_K_M	5	10.1 GB	High	C51
Q6_KBest for your GPU	6	11.5 GB	High	C50
Q8_0	8	15.0 GB	Very High	F0
F16	16	28.7 GB	Maximum	F0

Get started

Copy-paste commands to run GGUF SOLARized GraniStral 14B 1902 YeAM HCT on your machine.

Run

lms load hf-srs6901--gguf-solarized-granistral-14b-1902-yeam-hct && lms server start

Opções de upgrade

Hardware que roda bem GGUF SOLARized GraniStral 14B 1902 YeAM HCT

RX 7900 XT 20GBOpção econômica

20 GB VRAM (+4)800 GB/s (+240)

Raises estimated decode speed by about 91%.56.2 tok/s decodificação

Raises estimated decode speed by about 91%.

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

~$899 MSRP

RX 7900 XTX 24GBMelhor custo-benefício

24 GB VRAM (+8)960 GB/s (+400)

Raises estimated decode speed by about 174%.80.9 tok/s decodificação

Raises estimated decode speed by about 174%.

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

~$999 MSRP

Frequently asked questions

Can Intel Arc A770 16GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT?

Yes, Intel Arc A770 16GB can run GGUF SOLARized GraniStral 14B 1902 YeAM HCT with a C grade (Runs well). Expected decode speed: 29.5 tok/s.

How much VRAM does GGUF SOLARized GraniStral 14B 1902 YeAM HCT need?

GGUF SOLARized GraniStral 14B 1902 YeAM HCT (14B parameters) requires approximately 12.7 GB of memory with Q4_K_M quantization.

What is the best quantization for GGUF SOLARized GraniStral 14B 1902 YeAM HCT?

The recommended quantization for GGUF SOLARized GraniStral 14B 1902 YeAM HCT is Q4_K_M, which balances quality and memory efficiency.

What speed will GGUF SOLARized GraniStral 14B 1902 YeAM HCT run at on Intel Arc A770 16GB?

On Intel Arc A770 16GB, GGUF SOLARized GraniStral 14B 1902 YeAM HCT achieves approximately 29.5 tokens per second decode speed with a time-to-first-token of 6561ms using Q4_K_M quantization.

Can Intel Arc A770 16GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT for coding?

For coding workloads, GGUF SOLARized GraniStral 14B 1902 YeAM HCT on Intel Arc A770 16GB receives a C grade with 29.5 tok/s and 48K context.

What context window can GGUF SOLARized GraniStral 14B 1902 YeAM HCT use on Intel Arc A770 16GB?

On Intel Arc A770 16GB, GGUF SOLARized GraniStral 14B 1902 YeAM HCT can safely use up to 48K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if GGUF SOLARized GraniStral 14B 1902 YeAM HCT feels slow on Intel Arc A770 16GB?

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 A770 16GB for GGUF SOLARized GraniStral 14B 1902 YeAM HCT?

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 A770 16GB See all hardware for GGUF SOLARized GraniStral 14B 1902 YeAM HCT

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