Llama 3.3 70B vs Qwen 3 32B — the size-vs-architecture tradeoff
Single 24 GB card → Qwen 3 32B (no question). 48 GB+ → Llama 3.3 70B if you need the parameter quality, Qwen 3 32B if you need the speed.
Llama 3.3 70B Instruct
70B params · Llama 3.3 Community License · llama
Qwen 3 32B
32B 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 3.3 70B Instruct | Qwen 3 32B |
|---|---|---|
RTX 3090 (24 GB) Used $700-1,000 — the local-AI workhorse | OOM | Q3 only, 2K ctx |
RTX 4090 (24 GB) Used $1,400-1,900 — current consumer flagship | OOM | Q3 only, 2K ctx |
RTX 5090 (32 GB) Retail $2,000-2,500 — Blackwell consumer | OOM | Q4 @ 4K, tight |
Mac M4 Max (64 GB unified) $4,000-5,000 — Apple Silicon flagship | Q4 @ 2K, tight | Q4 @ 16K, tight |
Dual RTX 3090 (48 GB pooled) ~$1,500-2,000 — workstation budget build | OOM | Q4 @ 8K, tight |
H100 (80 GB) $25K+ — datacenter / cloud rental tier | Q4 @ 4K ctx | Q4 @ 16K 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.
Classic size-vs-recency tradeoff. Llama 3.3 70B has the parameter advantage and Meta's strong instruction-following post-training. Qwen 3 32B is half the size with newer training data and a sharper reasoning posture at the cost of raw parameter count.
Where it matters: Llama 3.3 70B needs a 48 GB minimum (dual 3090 / M-series 96 GB+); Qwen 3 32B fits on a single 24 GB card. The single-card simplicity gap is the operator's real-world delta — Qwen 3 32B at 24 GB beats Llama 3.3 70B at 48 GB if you only have one slot.
The verdict for chat workloadsPick → Qwen 3 32B
decisive edge for Qwen 3 32B — wins 4 of 10 dimensions (1 loss, 5 ties). Verdict reasoning below — no percentage shown on purpose (why).
Qwen 3 32B is the better fit for chat on the dimensions we score, taking 4 of 10 rows. The weighted score (5% vs 55%) 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 3.3 70B Instruct | Qwen 3 32B | Edge |
|---|---|---|---|
Editorial rating (1-10) Editor rating — single human assessment across reasoning, fluency, tool-use, instruction-following. | 9.1 | 8.9 | tie |
Parameters (B) | 70.0B | 32.0B | Llama |
Context length (tokens) | 131K | 131K | tie |
License (commercial OK?) | ✓ Llama 3.3 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. | 13.1 tok/s | 28.7 tok/s | Qwen |
Fits comfortably on NVIDIA GeForce RTX 4090? | ✕ 35.2 GB short | ✕ 3.0 GB short | Qwen |
Cost to run (local, Q4) Smaller model → less VRAM + less electricity per token. Cross-reference with /cost-vs-cloud for $-anchored math. | 42.3 GB at Q4_K_M | 19.3 GB at Q4_K_M | Qwen |
Community popularity Editorial popularity score — proxy for runtime support breadth + community recipe availability. | 93 | 92 | tie |
Multimodal support | text only | text only | tie |
Released | 2024-12-06 | 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 |
|---|---|---|
| 16 GB | → Qwen 3 32B | Qwen 3 32B at Q3_K_M is workable; Llama 3.3 70B isn't. |
| 24 GB | → Qwen 3 32B | Qwen 3 32B fits comfortably at Q4 with full context; Llama 3.3 70B does not. |
| 32 GB (RTX 5090) | → Qwen 3 32B | 5090's bandwidth makes Qwen 3 32B fly. Llama 3.3 70B still tight at this tier. |
| 48 GB+ (dual 3090) | → Llama 3.3 70B Instruct | Now Llama 3.3's parameter quality is unlocked. Pick it for hard-task workloads. |
Is Llama 3.3 70B worth the extra VRAM over Qwen 3 32B?
For pure quality on hard tasks (long-context reasoning, complex instruction-following), Llama 3.3 70B wins per token. For everything else — speed, single-card simplicity, daily-driver chat, agentic loops where throughput matters — Qwen 3 32B's newer training and 24 GB footprint often wins. If you only have one GPU slot, pick Qwen.
Which one is better for coding?
Neither is the strict best choice. For coding specifically, Qwen 2.5 Coder 32B or Qwen 3 Coder (when it ships) is the right pick. Between these two: Qwen 3 32B's reasoning helps on multi-step refactors; Llama 3.3 70B's parameter count helps on broader codebase context.
What about Llama 3.3 70B on a single RTX 5090 (32 GB)?
Tight. Q4_K_M weights are ~39 GB — overflows even the 5090's 32 GB. You'd need Q3_K_M or partial CPU offload, both of which materially slow throughput. The 5090 is a better Qwen 3 32B card than a Llama 3.3 70B card.
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.