Can internlm JanusCoder 14B run on AMD Instinct MI210 64GB?

YES — Runs Great

C47Usable

Estimated from fit model

internlm JanusCoder 14B needs ~17.5 GB VRAM. AMD Instinct MI210 64GB has 64.0 GB. With Q4_K_M quantization, expect ~130 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 17.5 GB, 130.4 tok/s, Runs well

17.5 GB required64.0 GB available

27% VRAM used

Fit status

Runs well

Decode

130.4 tok/s

TTFT

1484 ms

Safe context

470K

Memory

17.5 GB / 64.0 GB

Memory breakdown

Weights8.5 GB

KV Cache1.6 GB

Runtime0.9 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelsinternlm JanusCoder 14B on AMD Instinct MI210 64GB
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: 130.4 tok/s decode · 1.5s TTFT (warm) · 326 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	130.4 tok/s	810 ms	470K
Coding	C	Runs well	130.4 tok/s	1484 ms	470K
Agentic Coding	C	Runs well	130.4 tok/s	2159 ms	470K
Reasoning	C	Runs well	130.4 tok/s	1754 ms	470K
RAG	C	Runs well	130.4 tok/s	2699 ms	470K

Quantization options

How internlm JanusCoder 14B (14B params) fits at each quantization level on AMD Instinct MI210 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	C40
Q3_K_S	3	6.9 GB	Low	C40
NVFP4	4	7.8 GB	Medium	C40
Q4_K_M	4	8.5 GB	Medium	C41
Q5_K_M	5	10.1 GB	High	C41
Q6_K	6	11.5 GB	High	C41
Q8_0	8	15.0 GB	Very High	C42
F16Best for your GPU	16	28.7 GB	Maximum	C45

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

Frequently asked questions

Can AMD Instinct MI210 64GB run internlm JanusCoder 14B?

Yes, AMD Instinct MI210 64GB can run internlm JanusCoder 14B with a C grade (Runs well). Expected decode speed: 130.4 tok/s.

How much VRAM does internlm JanusCoder 14B need?

internlm JanusCoder 14B (14B parameters) requires approximately 17.5 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 AMD Instinct MI210 64GB?

On AMD Instinct MI210 64GB, internlm JanusCoder 14B achieves approximately 130.4 tokens per second decode speed with a time-to-first-token of 1484ms using Q4_K_M quantization.

Can AMD Instinct MI210 64GB run internlm JanusCoder 14B for coding?

For coding workloads, internlm JanusCoder 14B on AMD Instinct MI210 64GB receives a C grade with 130.4 tok/s and 470K context.

What context window can internlm JanusCoder 14B use on AMD Instinct MI210 64GB?

On AMD Instinct MI210 64GB, internlm JanusCoder 14B can safely use up to 470K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for AMD Instinct MI210 64GB See all hardware for internlm JanusCoder 14B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

130.4 tok/s

810 ms

470K

Coding

Runs well

130.4 tok/s

1484 ms

470K

Agentic Coding

Runs well

130.4 tok/s

2159 ms

470K

Reasoning

Runs well

130.4 tok/s

1754 ms

470K

RAG

Runs well

130.4 tok/s

2699 ms

470K

Quantization options

How internlm JanusCoder 14B (14B params) fits at each quantization level on AMD Instinct MI210 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	C40
Q3_K_S	3	6.9 GB	Low	C40
NVFP4	4	7.8 GB	Medium	C40
Q4_K_M	4	8.5 GB	Medium	C41
Q5_K_M	5	10.1 GB	High	C41
Q6_K	6	11.5 GB	High	C41
Q8_0	8	15.0 GB	Very High	C42
F16Best for your GPU	16	28.7 GB	Maximum	C45

Frequently asked questions

Can AMD Instinct MI210 64GB run internlm JanusCoder 14B?

Yes, AMD Instinct MI210 64GB can run internlm JanusCoder 14B with a C grade (Runs well). Expected decode speed: 130.4 tok/s.

How much VRAM does internlm JanusCoder 14B need?

internlm JanusCoder 14B (14B parameters) requires approximately 17.5 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 AMD Instinct MI210 64GB?

On AMD Instinct MI210 64GB, internlm JanusCoder 14B achieves approximately 130.4 tokens per second decode speed with a time-to-first-token of 1484ms using Q4_K_M quantization.

Can AMD Instinct MI210 64GB run internlm JanusCoder 14B for coding?

For coding workloads, internlm JanusCoder 14B on AMD Instinct MI210 64GB receives a C grade with 130.4 tok/s and 470K context.

What context window can internlm JanusCoder 14B use on AMD Instinct MI210 64GB?

On AMD Instinct MI210 64GB, internlm JanusCoder 14B can safely use up to 470K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.