Can Yi Coder 1.5B run on RTX 4080 Super 16GB?

YES — Runs Great

C42Usable

Estimated from fit model

Yi Coder 1.5B needs ~3.6 GB VRAM. RTX 4080 Super 16GB has 16.0 GB. With Q4_K_M quantization, expect ~24 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) — 3.6 GB, 24.0 tok/s, Runs well

3.6 GB required16.0 GB available

23% VRAM used

Fit status

Runs well

Decode

24.0 tok/s

TTFT

8067 ms

Safe context

1.1M

Memory

3.6 GB / 16.0 GB

Memory breakdown

Weights0.9 GB

KV Cache0.2 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsYi Coder 1.5B on RTX 4080 Super 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: 24.0 tok/s decode · 8.1s TTFT (warm) · 60 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	24.0 tok/s	4400 ms	1.0M
Coding	C	Runs well	24.0 tok/s	8067 ms	1.1M
Agentic Coding	C	Runs well	24.0 tok/s	11733 ms	1.1M
Reasoning	C	Runs well	24.0 tok/s	9533 ms	1.1M
RAG	C	Runs well	24.0 tok/s	14667 ms	1.1M

Inference speed

Yi Coder 1.5B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Yi Coder 1.5B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~29 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	28.5	Fits
RTX 4090 24GB	24 GB	Q4_K_M	24.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	24.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	21.0	Fits
RTX 4070 12GB	12 GB	Q4_K_M	21.0	Fits
RTX 3060 12GB	12 GB	Q4_K_M	21.0	Fits
RTX 4060 8GB	8 GB	Q4_K_M	21.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	21.0	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	21.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	21.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	21.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	21.0	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	21.0	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	21.0	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	21.0	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	21.0	Fits

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 Yi Coder 1.5B (1.5B params) fits at each quantization level on RTX 4080 Super 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.6 GB	Low	C45
Q3_K_S	3	0.7 GB	Low	C45
NVFP4	4	0.8 GB	Medium	C45
Q4_K_M	4	0.9 GB	Medium	C45
Q5_K_M	5	1.1 GB	High	C45
Q6_K	6	1.2 GB	High	C45
Q8_0	8	1.6 GB	Very High	C46
F16Best for your GPU	16	3.1 GB	Maximum	C47

Get started

Copy-paste commands to run Yi Coder 1.5B on your machine.

Run

lms load hf-lmstudio-community--yi-coder-1-5b-gguf && lms server start

アップグレードオプション

Yi Coder 1.5Bを快適に動かすハードウェア

MacBook Pro M3 24GBコスパ重視

24 GB Unified (+8)

This setup is broadly balanced for this model.21 tok/sデコード

〜$1,099 MSRP

MacBook Air M3 24GBコスパ最良

24 GB Unified (+8)

This setup is broadly balanced for this model.21 tok/sデコード

〜$1,099 MSRP

Frequently asked questions

Can RTX 4080 Super 16GB run Yi Coder 1.5B?

Yes, RTX 4080 Super 16GB can run Yi Coder 1.5B with a C grade (Runs well). Expected decode speed: 24.0 tok/s.

How much VRAM does Yi Coder 1.5B need?

Yi Coder 1.5B (1.5B parameters) requires approximately 3.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi Coder 1.5B?

The recommended quantization for Yi Coder 1.5B is Q4_K_M, which balances quality and memory efficiency.

What speed will Yi Coder 1.5B run at on RTX 4080 Super 16GB?

On RTX 4080 Super 16GB, Yi Coder 1.5B achieves approximately 24.0 tokens per second decode speed with a time-to-first-token of 8067ms using Q4_K_M quantization.

Can RTX 4080 Super 16GB run Yi Coder 1.5B for coding?

For coding workloads, Yi Coder 1.5B on RTX 4080 Super 16GB receives a C grade with 24.0 tok/s and 1.1M context.

What context window can Yi Coder 1.5B use on RTX 4080 Super 16GB?

On RTX 4080 Super 16GB, Yi Coder 1.5B can safely use up to 1.1M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 4080 Super 16GB See all hardware for Yi Coder 1.5B

Embed this result▼

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

<iframe src="https://willitrunai.com/embed/hf-lmstudio-community--yi-coder-1-5b-gguf-on-rtx-4080-super-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: