Can Yi Coder 1.5B Chat run on RTX 2060 6GB?

YES — Runs Great

C48Usable

Estimated from fit model

Yi Coder 1.5B Chat needs ~2.9 GB VRAM. RTX 2060 6GB has 6.0 GB. With Q4_K_M quantization, expect ~21 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: 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) — 2.9 GB, 21.0 tok/s, Runs well

2.9 GB required6.0 GB available

48% VRAM used

Fit status

Runs well

Decode

21.0 tok/s

TTFT

9219 ms

Safe context

299K

Memory

2.9 GB / 6.0 GB

Memory breakdown

Weights0.9 GB

KV Cache0.2 GB

Runtime1.2 GB

Headroom0.6 GB

See how fast it feels

See how fast it feelsYi Coder 1.5B Chat on RTX 2060 6GB
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: 21.0 tok/s decode · 9.2s TTFT (warm) · 53 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	21.0 tok/s	5029 ms	263K
Coding	C	Runs well	21.0 tok/s	9219 ms	299K
Agentic Coding	C	Runs well	21.0 tok/s	13410 ms	299K
Reasoning	C	Runs well	21.0 tok/s	10895 ms	299K
RAG	C	Runs well	21.0 tok/s	16762 ms	299K

Inference speed

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

Estimated decode speed (tokens/sec) for Yi Coder 1.5B Chat 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 Chat (1.5B params) fits at each quantization level on RTX 2060 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.6 GB	Low	C52
Q3_K_S	3	0.7 GB	Low	C52
NVFP4	4	0.8 GB	Medium	C52
Q4_K_M	4	0.9 GB	Medium	C52
Q5_K_M	5	1.1 GB	High	C53
Q6_K	6	1.2 GB	High	C53
Q8_0	8	1.6 GB	Very High	C54
F16Best for your GPU	16	3.1 GB	Maximum	C54

Get started

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

Run

lms load hf-maziyarpanahi--yi-coder-1-5b-chat-gguf && lms server start

Frequently asked questions

Can RTX 2060 6GB run Yi Coder 1.5B Chat?

Yes, RTX 2060 6GB can run Yi Coder 1.5B Chat with a C grade (Runs well). Expected decode speed: 21.0 tok/s.

How much VRAM does Yi Coder 1.5B Chat need?

Yi Coder 1.5B Chat (1.5B parameters) requires approximately 2.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi Coder 1.5B Chat?

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

What speed will Yi Coder 1.5B Chat run at on RTX 2060 6GB?

On RTX 2060 6GB, Yi Coder 1.5B Chat achieves approximately 21.0 tokens per second decode speed with a time-to-first-token of 9219ms using Q4_K_M quantization.

Can RTX 2060 6GB run Yi Coder 1.5B Chat for coding?

For coding workloads, Yi Coder 1.5B Chat on RTX 2060 6GB receives a C grade with 21.0 tok/s and 299K context.

What context window can Yi Coder 1.5B Chat use on RTX 2060 6GB?

On RTX 2060 6GB, Yi Coder 1.5B Chat can safely use up to 299K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 2060 6GB See all hardware for Yi Coder 1.5B Chat

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