Will It Run AI

Can internlm JanusCoder 14B run on Radeon RX 6850M XT 12GB?

YES — With Offload

C49Usable
Estimated from fit model

internlm JanusCoder 14B needs ~12.3 GB VRAM. Radeon RX 6850M XT 12GB has 12.0 GB. With Q4_K_M quantization, expect ~20 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload
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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) 12.3 GB, 20.2 tok/s, Runs with offload (needs ~0.2 GB host RAM)
12.3 GB required12.0 GB available
103% VRAM needed

0.3 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.2 GB host RAM)

Decode

20.2 tok/s

TTFT

9567 ms

Safe context

13K

Memory

12.3 GB / 12.0 GB

Memory breakdown

Weights8.5 GB
KV Cache1.6 GB
Runtime0.9 GB
Headroom1.2 GB

See how fast it feels

See how fast it feelsinternlm JanusCoder 14B on Radeon RX 6850M XT 12GB
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: 20.2 tok/s decode · 9.6s TTFT (warm) · 51 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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.

Best improvement 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns with offload28.3 tok/s3728 ms13K
CodingCRuns with offload20.2 tok/s9567 ms13K
Agentic CodingDVery compromised (needs ~1.2 GB host RAM)15.5 tok/s18119 ms13K
ReasoningCRuns with offload (needs ~0.2 GB host RAM)20.2 tok/s11306 ms13K
RAGDVery compromised (needs ~1.2 GB host RAM)15.5 tok/s22648 ms13K

Quantization options

How internlm JanusCoder 14B (14B params) fits at each quantization level on Radeon RX 6850M XT 12GB (12.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.5 GB
LowC52
Q3_K_S
3
6.9 GB
LowC52
NVFP4
4
7.8 GB
MediumC51
Q4_K_MBest for your GPU
4
8.5 GB
MediumC51
Q5_K_M
5
10.1 GB
HighF0
Q6_K
6
11.5 GB
HighF0
Q8_0
8
15.0 GB
Very HighF0
F16
16
28.7 GB
MaximumF0

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

Frequently asked questions

Can Radeon RX 6850M XT 12GB run internlm JanusCoder 14B?

Yes, Radeon RX 6850M XT 12GB can run internlm JanusCoder 14B with a C grade (Runs with offload). Expected decode speed: 20.2 tok/s.

How much VRAM does internlm JanusCoder 14B need?

internlm JanusCoder 14B (14B parameters) requires approximately 12.3 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 RX 6850M XT 12GB?

On Radeon RX 6850M XT 12GB, internlm JanusCoder 14B achieves approximately 20.2 tokens per second decode speed with a time-to-first-token of 9567ms using Q4_K_M quantization.

Can Radeon RX 6850M XT 12GB run internlm JanusCoder 14B for coding?

For coding workloads, internlm JanusCoder 14B on Radeon RX 6850M XT 12GB receives a C grade with 20.2 tok/s and 13K context.

What context window can internlm JanusCoder 14B use on Radeon RX 6850M XT 12GB?

On Radeon RX 6850M XT 12GB, internlm JanusCoder 14B can safely use up to 13K 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 Radeon RX 6850M XT 12GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for Radeon RX 6850M XT 12GBSee all hardware for internlm JanusCoder 14B
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