Llama 3.1 8B vs Qwen 3 8B — the consumer-GPU default question
Fresh install in 2026 → Qwen 3 8B (sharper, newer). Max app ecosystem + the broadest fine-tune library → Llama 3.1 8B. Both fit a 12 GB card.
Llama 3.1 8B Instruct
8B params · Llama 3.1 Community License · llama
Qwen 3 8B
8B params · Apache 2.0 · qwen
Who wins each use case
Each row is the dimension-weighted verdict for that use case. Use case weights live in src/lib/model-battle/comparator.ts and are public.
What hardware actually runs each model
VRAM math against the canonical hardware ladder. The largest context window that fits with headroom at Q4_K_M appears in each cell.
| Hardware tier | Llama 3.1 8B Instruct | Qwen 3 8B |
|---|---|---|
RTX 3090 (24 GB) Used $700-1,000 — the local-AI workhorse | Q4 @ 32K, tight | Q4 @ 32K, tight |
RTX 4090 (24 GB) Used $1,400-1,900 — current consumer flagship | Q4 @ 32K, tight | Q4 @ 32K, tight |
RTX 5090 (32 GB) Retail $2,000-2,500 — Blackwell consumer | Q4 @ 32K ctx | Q4 @ 32K ctx |
Mac M4 Max (64 GB unified) $4,000-5,000 — Apple Silicon flagship | Q4 @ 32K ctx | Q4 @ 32K ctx |
Dual RTX 3090 (48 GB pooled) ~$1,500-2,000 — workstation budget build | Q4 @ 32K ctx | Q4 @ 32K ctx |
H100 (80 GB) $25K+ — datacenter / cloud rental tier | Q4 @ 32K ctx | Q4 @ 32K ctx |
On RTX 4090 @ Q4_K_M — bandwidth-derived estimate
Computed from each option's sustained TDP × predicted tok/s at $0.16/kWh. Cloud baseline: Claude Sonnet 4.6 (input + output).
Electricity-only cost — excludes the upfront hardware purchase, cooling, and amortized component depreciation. Hardware ROI math lives at /cost-vs-cloud; this line is for "is the marginal token cheaper than Claude?" not "should I buy this rig instead of paying Anthropic." MODELED ESTIMATE.
Both fit on a 12 GB card at Q4 with comfortable context. Both are open-weight under permissive licenses. The choice between them is style: Llama 3.1 8B has Meta's strong instruction-following + the broader fine-tune ecosystem (every coding-agent and chat-app supports it by default). Qwen 3 8B is the newer model with sharper reasoning posture and improved multilingual handling.
For a fresh install in 2026, Qwen 3 8B is the recency-default. For maximum app compatibility and the largest fine-tune library, Llama 3.1 8B remains the conservative pick.
The verdict for chat workloadsPick → Qwen 3 8B
slight edge for Qwen 3 8B — wins 1 of 10 dimensions (0 losses, 9 ties). Verdict reasoning below — no percentage shown on purpose (why).
Qwen 3 8B is the better fit for chat on the dimensions we score, taking 1 of 10 rows. The weighted score (0% vs 5%) reflects use-case priorities: quality (30%) + cost (20%) + speed (20%) anchor most of the call. Both models are worth running — this just tells you which one to reach for first.
| Dimension | Llama 3.1 8B Instruct | Qwen 3 8B | Edge |
|---|---|---|---|
Editorial rating (1-10) Editor rating — single human assessment across reasoning, fluency, tool-use, instruction-following. | 8.7 | 8.5 | tie |
Parameters (B) | 8.0B | 8.0B | tie |
Context length (tokens) | 131K | 131K | tie |
License (commercial OK?) | ✓ Llama 3.1 Community License | ✓ Apache 2.0 | tie |
Decode tok/s on NVIDIA GeForce RTX 4090 (Q4_K_M) Bandwidth-derived estimate. Smaller models stream faster on the same hardware. | 114.8 tok/s | 114.8 tok/s | tie |
Fits comfortably on NVIDIA GeForce RTX 4090? | ✓ 17.2 GB headroom | ✓ 17.2 GB headroom | tie |
Cost to run (local, Q4) Smaller model → less VRAM + less electricity per token. Cross-reference with /cost-vs-cloud for $-anchored math. | 4.8 GB at Q4_K_M | 4.8 GB at Q4_K_M | tie |
Community popularity Editorial popularity score — proxy for runtime support breadth + community recipe availability. | 95 | 91 | tie |
Multimodal support | text only | text only | tie |
Released | 2024-07-23 | 2025-04-29 | Qwen |
Which model wins on which VRAM tier. Picks update based on which one fits comfortably + which one’s strengths are unlocked by the available headroom.
| VRAM tier | Pick | Why |
|---|---|---|
| 8 GB | → Llama 3.1 8B Instruct | Both are tight at Q4 with 8 GB. Llama 3.1's slightly tighter post-training fits the available headroom marginally better. |
| 12 GB | → Qwen 3 8B | Sweet spot for either. Qwen 3 8B's reasoning + multilingual edge is the recency-default win. |
| 16 GB+ | → Qwen 3 8B | Plenty of headroom; pick Qwen 3 8B and run Llama 3.1 8B as a tool-compatibility sidecar. |
Llama 3.1 8B or Qwen 3 8B — which one to run as my daily driver?
Qwen 3 8B for fresh installs in 2026 (sharper reasoning, better multilingual). Llama 3.1 8B if you need the broadest app + fine-tune ecosystem compatibility — every local-AI app supports it by default. Both fit on a 12 GB card; switch between them costs nothing.
Which one has better tool-use / function-calling?
Qwen 3 8B was trained with tool-use as a first-class capability. Llama 3.1 8B supports tool-use but requires more careful prompt scaffolding to get reliable function calls. For agent loops with structured tool calls, Qwen 3 8B is the lower-friction pick.
Which one is better for non-English languages?
Qwen 3 8B was trained with broader multilingual coverage (notably stronger Chinese, Japanese, Korean, and Arabic). Llama 3.1 8B has solid coverage for the major European languages but trails on East Asian + Middle Eastern. If multilingual matters, Qwen.
Can I run both at the same time?
On 16 GB+ yes — two Ollama instances or one vLLM with both models. On 12 GB, you'll need to swap. The swap cost via Ollama (warm cache) is a few seconds; via vLLM cold-start it's significant. Plan for a single default model unless you have 16 GB+.
Comparison data computed from live catalog rows + the model-battle comparator (src/lib/model-battle/comparator.ts). For arbitrary pairings outside this curated list, use /model-battle to pick any two models + your hardware.