Can internlm JanusCoder 14B run on Radeon Pro W7800 32GB?

YES — Runs Great

C48Usable

Estimated from fit model

internlm JanusCoder 14B needs ~14.3 GB VRAM. Radeon Pro W7800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~40 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) — 14.3 GB, 39.8 tok/s, Runs well

14.3 GB required32.0 GB available

45% VRAM used

Fit status

Runs well

Decode

39.8 tok/s

TTFT

4865 ms

Safe context

189K

Memory

14.3 GB / 32.0 GB

Memory breakdown

Weights8.5 GB

KV Cache1.6 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsinternlm JanusCoder 14B on Radeon Pro W7800 32GB
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: 39.8 tok/s decode · 4.9s TTFT (warm) · 100 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	39.8 tok/s	2654 ms	189K
Coding	C	Runs well	39.8 tok/s	4865 ms	189K
Agentic Coding	C	Runs well	39.8 tok/s	7076 ms	189K
Reasoning	C	Runs well	39.8 tok/s	5750 ms	189K
RAG	C	Runs well	39.8 tok/s	8846 ms	189K

Quantization options

How internlm JanusCoder 14B (14B params) fits at each quantization level on Radeon Pro W7800 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	C43
Q3_K_S	3	6.9 GB	Low	C44
NVFP4	4

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

Upgrade options

Hardware that runs internlm JanusCoder 14B well

MacBook Pro M4 Max 48GBBudget pick

48 GB Unified (+16)

This setup is broadly balanced for this model.35.4 tok/s decode

~$2,499 MSRP

RTX PRO 5000 Blackwell 48GBBest value

48 GB VRAM (+16)1344 GB/s (+768)

Raises estimated decode speed by about 232%.132.2 tok/s decode

Raises estimated decode speed by about 232%.

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

~$4,999 MSRP

Frequently asked questions

Can Radeon Pro W7800 32GB run internlm JanusCoder 14B?

Yes, Radeon Pro W7800 32GB can run internlm JanusCoder 14B with a C grade (Runs well). Expected decode speed: 39.8 tok/s.

How much VRAM does internlm JanusCoder 14B need?

internlm JanusCoder 14B (14B parameters) requires approximately 14.3 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 Radeon Pro W7800 32GB?

On Radeon Pro W7800 32GB, internlm JanusCoder 14B achieves approximately 39.8 tokens per second decode speed with a time-to-first-token of 4865ms using Q4_K_M quantization.

Can Radeon Pro W7800 32GB run internlm JanusCoder 14B for coding?

For coding workloads, internlm JanusCoder 14B on Radeon Pro W7800 32GB receives a C grade with 39.8 tok/s and 189K context.

What context window can internlm JanusCoder 14B use on Radeon Pro W7800 32GB?

On Radeon Pro W7800 32GB, internlm JanusCoder 14B can safely use up to 189K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Radeon Pro W7800 32GB See all hardware for internlm JanusCoder 14B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-bartowski--internlm-januscoder-14b-gguf-on-radeon-pro-w7800-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: