MacBook Pro M3 Pro 18GBOpção econômica
18 GB Unified (+6)
C
This setup is broadly balanced for this model.21 tok/s decodificação
~$1,999 MSRP
Can Yi Coder 1.5B run on RTX 3500 Ada Laptop 12GB?
YES — Runs Great
C43Usable
Estimated from fit model
Yi Coder 1.5B needs ~3.5 GB VRAM. RTX 3500 Ada Laptop 12GB has 12.0 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.
Select quantization to explore
Q4_K_M (Medium quality) — 3.5 GB, 21.0 tok/s, Runs well
3.5 GB required12.0 GB available
29% VRAM used
Fit status
Runs well
Decode
21.0 tok/s
TTFT
9219 ms
Safe context
791K
Memory
3.5 GB / 12.0 GB
Memory breakdown
Weights0.9 GB
KV Cache0.2 GB
Runtime1.2 GB
Headroom1.2 GB
See how fast it feels
See how fast it feelsYi Coder 1.5B on RTX 3500 Ada Laptop 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: 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.
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 | 21.0 tok/s | 5029 ms | 695K |
| Coding | C | Runs well | 21.0 tok/s | 9219 ms | 791K |
| Agentic Coding | C | Runs well | 21.0 tok/s | 13410 ms | 791K |
| Reasoning | C | Runs well | 21.0 tok/s | 10895 ms | 791K |
| RAG | C | Runs well | 21.0 tok/s | 16762 ms | 791K |
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 3500 Ada Laptop 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.6 GB | Low | C46 |
Q3_K_S | 3 | 0.7 GB | Low | C47 |
NVFP4 | 4 | 0.8 GB | Medium | C47 |
Q4_K_M | 4 | 0.9 GB | Medium | C47 |
Q5_K_M | 5 | 1.1 GB | High | C47 |
Q6_K | 6 | 1.2 GB | High | C47 |
Q8_0 | 8 | 1.6 GB | Very High | C47 |
F16Best for your GPU | 16 | 3.1 GB | Maximum | C49 |
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 startOpções de upgrade
Hardware que roda bem Yi Coder 1.5B
Frequently asked questions
Can RTX 3500 Ada Laptop 12GB run Yi Coder 1.5B?
Yes, RTX 3500 Ada Laptop 12GB can run Yi Coder 1.5B with a C grade (Runs well). Expected decode speed: 21.0 tok/s.
How much VRAM does Yi Coder 1.5B need?
Yi Coder 1.5B (1.5B parameters) requires approximately 3.5 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 3500 Ada Laptop 12GB?
On RTX 3500 Ada Laptop 12GB, Yi Coder 1.5B achieves approximately 21.0 tokens per second decode speed with a time-to-first-token of 9219ms using Q4_K_M quantization.
Can RTX 3500 Ada Laptop 12GB run Yi Coder 1.5B for coding?
For coding workloads, Yi Coder 1.5B on RTX 3500 Ada Laptop 12GB receives a C grade with 21.0 tok/s and 791K context.
What context window can Yi Coder 1.5B use on RTX 3500 Ada Laptop 12GB?
On RTX 3500 Ada Laptop 12GB, Yi Coder 1.5B can safely use up to 791K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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