Can internlm JanusCoder 14B run on Intel Arc A770 16GB?

YES — Runs Great

C53Usable

Estimated from fit model

internlm JanusCoder 14B 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 feelsinternlm JanusCoder 14B on Intel Arc A770 16GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:

def quicksort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers))  # [1, 1, 2, 3, 6, 8, 10]

The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
    """Sort a list using the quicksort algorithm.

    Args:
        arr: List of integers to sort.

    Returns:
        A new sorted list.
    """
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

Added type hints for the input and return type, plus a Google-style docstring.
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

internlm JanusCoder 14B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for internlm JanusCoder 14B 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 internlm JanusCoder 14B (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 internlm JanusCoder 14B on your machine.

Run

lms load hf-bartowski--internlm-januscoder-14b-gguf && lms server start

Opções de upgrade

Hardware que roda bem internlm JanusCoder 14B

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 internlm JanusCoder 14B?

Yes, Intel Arc A770 16GB can run internlm JanusCoder 14B with a C grade (Runs well). Expected decode speed: 29.5 tok/s.

How much VRAM does internlm JanusCoder 14B need?

internlm JanusCoder 14B (14B parameters) requires approximately 12.7 GB of memory with Q4_K_M quantization.

What is the best quantization for internlm JanusCoder 14B?

The recommended quantization for internlm JanusCoder 14B is Q4_K_M, which balances quality and memory efficiency.

What speed will internlm JanusCoder 14B run at on Intel Arc A770 16GB?

On Intel Arc A770 16GB, internlm JanusCoder 14B 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 internlm JanusCoder 14B for coding?

For coding workloads, internlm JanusCoder 14B on Intel Arc A770 16GB receives a C grade with 29.5 tok/s and 48K context.

What context window can internlm JanusCoder 14B use on Intel Arc A770 16GB?

On Intel Arc A770 16GB, internlm JanusCoder 14B 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 internlm JanusCoder 14B 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 internlm JanusCoder 14B?

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 internlm JanusCoder 14B

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