Qwen 3.5 9B vs Llama 3.1 8B (2026) — VRAM, Speed, Quality & Which Should You Run?
Head-to-head: Qwen 3.5 9B vs Llama 3.1 8B for local inference. VRAM (5.5 vs 4.9 GB Q4), tokens/sec on RTX 4090, MMLU, context, multilingual, and which wins per use case.
If you have 8-16 GB of VRAM and want a modern general-purpose local LLM, the decision usually comes down to Qwen 3.5 9B or Llama 3.1 8B. Both are the "flagship small" option from their respective providers, both run on mainstream consumer hardware, and both are widely available on Ollama, LM Studio, and Hugging Face. Here's the head-to-head.
Quick answer
- Picking for today's 8GB GPU: Llama 3.1 8B. Tiny VRAM advantage + mature ecosystem.
- Picking for long-term quality: Qwen 3.5 9B. Better benchmarks, much longer context, stronger multilingual.
- Picking for Apple Silicon: Qwen 3.5 9B. MLX support + 100+ language coverage matches Mac-user demographic better.
- Picking for coding: Qwen 3.5 9B (or a Qwen 3 Coder variant). Llama 3.1 8B lags on current coding benchmarks.
Specs side by side
| Spec | Qwen 3.5 9B | Llama 3.1 8B |
|---|---|---|
| Total parameters | 9B | 8B |
| Architecture | Dense transformer | Dense transformer |
| Context window (native) | 262,144 | 128,000 |
| Training data cutoff | Dec 2025 | ~Dec 2023 |
| Provider | Alibaba Cloud | Meta |
| License | Apache 2.0 | Llama 3.1 Community License |
| Multilingual | 100+ languages | ~25 languages strong |
| Native modalities | Text | Text |
License nuance: Apache 2.0 is more permissive — no restrictions on commercial use. Llama 3.1's license has a 700M monthly active users cap (most users never hit it) and some restrictions on training other models from Llama outputs.
VRAM by quantization
| Quant | Qwen 3.5 9B | Llama 3.1 8B | Difference |
|---|---|---|---|
| Q4_K_M | 5.5 GB | 4.9 GB | +0.6 GB |
| Q5_K_M | 6.5 GB | 5.9 GB | +0.6 GB |
| Q6_K | 7.4 GB | 6.7 GB | +0.7 GB |
| Q8_0 | 9.6 GB | 8.5 GB | +1.1 GB |
| FP16 | 18.5 GB | 16.2 GB | +2.3 GB |
Both fit on 8 GB GPUs at Q4. The 0.6 GB delta only matters if you're squeezing Qwen 3.5 9B onto a 6 GB card (doable at Q4 but tight for context) — Llama 3.1 8B has slightly more headroom there.
Speed on real hardware
Community-reported tokens/second at Q4_K_M via llama.cpp:
| GPU | Qwen 3.5 9B | Llama 3.1 8B | Winner |
|---|---|---|---|
| RTX 4060 8GB | ~40-55 tok/s | ~42-55 tok/s | Tie |
| RTX 3060 12GB | ~40 tok/s | ~44 tok/s | Llama +10% |
| RTX 4070 12GB | ~70 tok/s | ~72 tok/s | Tie |
| RTX 4090 24GB | ~125 tok/s | ~130 tok/s | Tie |
| RTX 5090 32GB | ~170 tok/s | ~180 tok/s | Llama slight |
| M4 Max 36GB | ~60-80 tok/s | ~60-80 tok/s | Tie |
On small models, speed is memory-bandwidth-limited, not compute-limited. Both models are similar architectures so throughput tracks hardware, not model choice.
Quality benchmarks
Published benchmark scores (higher is better):
| Benchmark | Qwen 3.5 9B | Llama 3.1 8B |
|---|---|---|
| MMLU (5-shot) | ~75 | ~68 |
| MMLU-Pro | ~45 | ~37 |
| HumanEval (coding) | ~72 | ~62 |
| GSM8K (math) | ~80 | ~75 |
| MATH (hard math) | ~48 | ~30 |
| HellaSwag (common-sense) | ~82 | ~83 |
| IFEval (instruction follow) | ~75 | ~73 |
Takeaway: Qwen 3.5 9B wins on general knowledge (MMLU), coding, and advanced math. Llama 3.1 8B is equivalent or slightly ahead on common-sense / natural reasoning. Both follow instructions well.
Use-case recommendations
Daily coding assistant
Pick Qwen 3.5 9B. ~10% better on HumanEval and Python-specific tasks. If coding is your dominant workload and you have more VRAM, step up to Qwen 3.5 27B (16.5 GB at Q4) or a Qwen 3 Coder variant.
Long-document / RAG
Pick Qwen 3.5 9B. 262K context is 2× Llama's 128K. Processing 200-page PDFs, whole codebases, or multi-turn conversations with history works out of the box.
Casual chat
Tie. Both handle general conversation well. Pick the one your Ollama/LM Studio library already has cached.
Multi-language
Pick Qwen 3.5 9B. 100+ languages with quality parity across Chinese, Spanish, Portuguese, Japanese, Korean, Arabic, Hindi. Llama 3.1 8B is English-dominant with useful coverage of ~25 top-tier languages.
Roleplay / creative writing
Pick Llama 3.1 8B or a community fine-tune of it. The Llama 3 ecosystem has dozens of creative-writing fine-tunes that outperform base models on narrative tasks. Qwen has fewer such fine-tunes available.
Agent / tool-use workflows
Mild edge to Llama 3.1 8B. More community agent frameworks (LangChain, CrewAI) have battle-tested Llama 3 configurations. Qwen 3.5 9B works but may need prompt tweaking for agent scaffolds optimized for Llama.
Setup commands
Qwen 3.5 9B
ollama run qwen3.5:9b
# or llama.cpp Q4_K_M
huggingface-cli download unsloth/Qwen3.5-9B-GGUF Qwen3.5-9B-Q4_K_M.gguf --local-dir models/
# or MLX on Mac
pip install mlx-lm && mlx_lm.generate --model mlx-community/Qwen3.5-9B-MLX-4bit --prompt "..."
Llama 3.1 8B
ollama run llama3.1:8b
# or llama.cpp Q4_K_M
huggingface-cli download bartowski/Meta-Llama-3.1-8B-Instruct-GGUF Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf --local-dir models/
# or MLX on Mac
pip install mlx-lm && mlx_lm.generate --model mlx-community/Meta-Llama-3.1-8B-Instruct-4bit --prompt "..."
Bottom line
| If you prioritize... | Pick |
|---|---|
| Latest-generation quality | Qwen 3.5 9B |
| Mature ecosystem + community fine-tunes | Llama 3.1 8B |
| Long context (>128K) | Qwen 3.5 9B |
| Tight VRAM (6-8 GB) | Llama 3.1 8B |
| Multilingual (100+ langs) | Qwen 3.5 9B |
| Roleplay / creative writing | Llama 3.1 8B (via fine-tunes) |
| Coding / math benchmarks | Qwen 3.5 9B |
| Permissive license for commercial | Qwen 3.5 9B (Apache 2.0) |
For most 2026 users the answer is Qwen 3.5 9B — better benchmarks, 2× context, better multilingual, more permissive license. Llama 3.1 8B remains the better pick if you're using Llama-specific tooling, need maximum VRAM headroom on a 6 GB card, or want a creative-writing fine-tune.