Can internlm JanusCoder 14B run on RX 9060 XT 16GB?

YES — Runs Great

C52Usable

Estimated from fit model

internlm JanusCoder 14B needs ~12.7 GB VRAM. RX 9060 XT 16GB has 16.0 GB. With Q4_K_M quantization, expect ~24 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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, 23.6 tok/s, Runs well

12.7 GB required16.0 GB available

79% VRAM used

Fit status

Runs well

Decode

23.6 tok/s

TTFT

8201 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 RX 9060 XT 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: 23.6 tok/s decode · 8.2s TTFT (warm) · 59 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	23.6 tok/s	4473 ms	48K
Coding	C	Runs well	23.6 tok/s	8201 ms	48K
Agentic Coding	C	Tight fit	23.6 tok/s	11929 ms	48K
Reasoning	C	Runs well	23.6 tok/s	9692 ms	48K
RAG	C	Tight fit	23.6 tok/s	14911 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 RX 9060 XT 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

Upgrade options

Hardware that runs internlm JanusCoder 14B well

RX 7900 XT 20GBBudget pick

20 GB VRAM (+4)800 GB/s (+480)

Raises estimated decode speed by about 138%.56.2 tok/s decode

Raises estimated decode speed by about 138%.

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

~$899 MSRP

RX 7900 XTX 24GBBest value

24 GB VRAM (+8)960 GB/s (+640)

Raises estimated decode speed by about 243%.80.9 tok/s decode

Raises estimated decode speed by about 243%.

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

~$999 MSRP

Frequently asked questions

Can RX 9060 XT 16GB run internlm JanusCoder 14B?

Yes, RX 9060 XT 16GB can run internlm JanusCoder 14B with a C grade (Runs well). Expected decode speed: 23.6 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 RX 9060 XT 16GB?

On RX 9060 XT 16GB, internlm JanusCoder 14B achieves approximately 23.6 tokens per second decode speed with a time-to-first-token of 8201ms using Q4_K_M quantization.

Can RX 9060 XT 16GB run internlm JanusCoder 14B for coding?

For coding workloads, internlm JanusCoder 14B on RX 9060 XT 16GB receives a C grade with 23.6 tok/s and 48K context.

What context window can internlm JanusCoder 14B use on RX 9060 XT 16GB?

On RX 9060 XT 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.

See all results for RX 9060 XT 16GB See all hardware for internlm JanusCoder 14B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/hf-bartowski--internlm-januscoder-14b-gguf-on-rx-9060-xt-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

23.6 tok/s

4473 ms

48K

Coding

Runs well

23.6 tok/s

8201 ms

48K

Agentic Coding

Tight fit

23.6 tok/s

11929 ms

48K

Reasoning

Runs well

23.6 tok/s

9692 ms

48K

RAG

Tight fit

23.6 tok/s

14911 ms

48K

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

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

Quantization options

How internlm JanusCoder 14B (14B params) fits at each quantization level on RX 9060 XT 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

Frequently asked questions

Can RX 9060 XT 16GB run internlm JanusCoder 14B?

Yes, RX 9060 XT 16GB can run internlm JanusCoder 14B with a C grade (Runs well). Expected decode speed: 23.6 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 RX 9060 XT 16GB?

On RX 9060 XT 16GB, internlm JanusCoder 14B achieves approximately 23.6 tokens per second decode speed with a time-to-first-token of 8201ms using Q4_K_M quantization.

Can RX 9060 XT 16GB run internlm JanusCoder 14B for coding?

For coding workloads, internlm JanusCoder 14B on RX 9060 XT 16GB receives a C grade with 23.6 tok/s and 48K context.

What context window can internlm JanusCoder 14B use on RX 9060 XT 16GB?

On RX 9060 XT 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.