RTX 4060 8GB Token Speed: Coding LLM Benchmarks

Mar 27, 2026

I bought an RTX 4060 8GB for local LLM inference, and the first question I had was: how fast will my coding assistants actually run? Vendor specs don’t tell you much about real-world token generation speed. I needed to benchmark my own setup and figure out which models balance speed and coding quality.

Here’s what I found after testing multiple models and configurations.

What Token Speed Actually Means

Tokens per second (t/s) measures how fast a model generates text. One token is roughly 4 characters or 0.75 words. A 30 t/s model generates about 22 words per second.

There are two speed metrics:

Prompt evaluation speed: How fast the model reads your input
Token generation speed: How fast the model writes new text

Generation speed matters most for interactive coding because that’s when you’re waiting for output. Prompt evaluation only affects the initial processing time.

For a responsive coding assistant, you want at least 15 t/s. Below that, the delay becomes noticeable and breaks your flow.

Measuring Your GPU Inference Speed

Ollama has a built-in verbose mode that shows exact token speeds:

ollama run qwen2.5-coder:7b --verbose

After any response, you’ll see output like:

eval rate: 28.45 tokens/second
prompt eval rate: 45.12 tokens/second

The “eval rate” is your token generation speed. The “prompt eval rate” is how fast it processed your input.

For automated benchmarking, I wrote a simple script:

#!/bin/bash
# Test token generation speed for a model

MODEL="qwen2.5-coder:7b"
PROMPT="Write a Python function to implement binary search"

echo "Testing $MODEL..."
ollama run $MODEL --verbose "$PROMPT" 2>&1 | grep -E "(eval rate|prompt eval rate)"

Run this multiple times to get consistent results. GPU warm-up and background processes affect the first run.

You can also monitor GPU utilization during inference:

watch -n 1 nvidia-smi

This helps confirm the model is actually using your GPU and not falling back to CPU inference.

RTX 4060 8GB Benchmark Results

I tested popular coding models with Q4_K_M quantization (the best balance of speed and quality). Here are the results:

Models That Fit in 8GB VRAM

Model	Quantization	VRAM Used	Token Speed	Coding Quality
Qwen2.5-Coder 7B	Q4_K_M	~5GB	28-35 t/s	Good
Qwen 3.5 8B	Q4_K_M	~6GB	25-32 t/s	Good
Qwen 3.5 9B	Q4_K_M	~7GB	22-28 t/s	Very Good
DeepSeek-Coder 6.7B	Q4_K_M	~5GB	30-35 t/s	Good

These models run entirely on the GPU. No CPU offloading needed. The 7B models hit the sweet spot of 30+ t/s with decent coding ability.

Models Requiring CPU+GPU Hybrid

Model	Quantization	VRAM + RAM	Token Speed	Coding Quality
Qwen 3.5 32B	Q4_K_M	8GB + 24GB	10-18 t/s	Excellent
Qwen2.5-Coder 32B	Q4_K_M	8GB + 24GB	7-15 t/s	Excellent
Qwen 3.5 35B	Q4_K_M	8GB + 28GB	7-15 t/s	Excellent

These larger models need system RAM because they exceed 8GB VRAM. The speed penalty is significant—about 50% slower than pure GPU inference—but the coding quality is much better.

I noticed hybrid inference speeds vary based on system RAM speed. My DDR4-3200 setup performs differently than DDR5 would.

The Speed vs Quality Trade-off

For coding assistance, here’s how I choose:

Priority	Model	Speed	Why Choose It
Speed first	Qwen 3.5 4B	40+ t/s	Quick answers, limited complexity
Balanced	Qwen2.5-Coder 7B	28-35 t/s	Good speed, solid coding
Quality first	Qwen 3.5 32B	10-18 t/s	Best code, slower response

The 7B models feel responsive for real-time coding. The 32B models feel sluggish but produce better code for complex tasks.

What Affects Token Speed

Model Size

Larger models require more computation per token. A 32B model is roughly 4-5x slower than a 7B model on the same hardware.

This is linear: double the parameters, roughly double the inference time.

Quantization Level

Q4_K_M is my default choice. Here’s how quantization affects speed:

Quantization	Relative Speed	Quality Loss
Q4_K_M	Baseline	Minimal
Q5_K_M	~10% slower	Slightly better
Q8_0	~20% slower	Near-original
FP16	~40% slower	No loss

Q4_K_M gives you 4-bit precision with minimal quality degradation. I haven’t noticed meaningful quality differences between Q4_K_M and Q5 for coding tasks.

GPU Layers Configuration

Ollama uses an environment variable to control GPU offloading:

# All layers on GPU (fastest, needs VRAM)
OLLAMA_GPU_LAYERS=35 ollama run qwen2.5-coder:7b

# Partial offload for larger models
OLLAMA_GPU_LAYERS=20 ollama run qwen3.5:32b

More GPU layers means faster inference but requires more VRAM. If you set this too high, the model won’t load.

Hardware Variables

Desktop GPUs are 1.5-2x faster than laptop variants with the same name. Laptop GPUs thermal throttle and have lower power limits.

For hybrid inference (CPU+GPU), RAM speed matters. DDR5 systems will outperform DDR4 for models exceeding VRAM.

llama.cpp vs Ollama Performance

I tested both tools with the same models:

Tool	Speed	Setup Difficulty
llama.cpp	Fastest	Higher (CLI only)
Ollama	~10-20% slower	Easy
LM Studio	Similar to Ollama	Easy (GUI)

Why llama.cpp is faster:

More aggressive kernel optimizations
Better memory management
Access to third-party quantizations like Unsloth
Multiple GPU backend options

I use Ollama for convenience and llama.cpp when I need maximum performance. The 10-20% difference isn’t worth the setup complexity for daily use.

How Much Speed Do You Actually Need

Based on my experience:

Use Case	Minimum Speed	Comfortable Speed
Real-time coding assistant	15+ t/s	25+ t/s
Code review/analysis	10+ t/s	20+ t/s
Batch code generation	5+ t/s	10+ t/s
Background processing	Any	Any

At 15 t/s, I notice the delay but can work with it. At 25+ t/s, the response feels natural for interactive coding.

Choosing the Right Model for RTX 4060

For my RTX 4060 8GB setup, I settled on this approach:

Daily coding: Qwen2.5-Coder 7B at 28-35 t/s. Fast enough for real-time assistance, good enough quality for most tasks.

Complex refactoring: Qwen 3.5 32B at 10-18 t/s. Slower but handles complex logic and architectural decisions better.

Quick questions: Qwen 3.5 4B at 40+ t/s. Nearly instant responses for simple queries.

The RTX 4060 can’t compete with 16GB+ cards for large models, but it handles the 7B-9B range perfectly. For coding assistance, that’s often enough.

Key Takeaways

RTX 4060 8GB delivers 25-35 t/s with 7B-9B coding models in pure GPU mode
32B+ models run at 10-18 t/s with CPU+GPU hybrid inference
Use --verbose flag with Ollama to measure your actual speed
Q4_K_M quantization is the best balance of speed and quality
Desktop GPUs significantly outperform laptop variants
llama.cpp offers 10-20% better performance than Ollama if you’re willing to configure it

The speed vs quality trade-off is personal. Test different models and see what feels right for your workflow.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!