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Compare any two local AI models

Pick any two open-weight models. Get a 10-dimension matrix + a hardware-fit table showing where each one runs across 8 common GPU tiers — 12 GB consumer cards to 192 GB Mac Studio M3 Ultra.

PICK ANY TWO MODELS
★ EDITORIAL PAGE EXISTS FOR THIS PAIR
Llama 3.3 70B vs Qwen 3 32B — the size-vs-architecture tradeoff

Hand-written editorial verdict, multi-paragraph framing, 3 FAQ entries, hardware-tier decision rule. Stricter depth than what the comparator alone produces.

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Live · DB-driven2 min read
TL;DR

For chat: Qwen 3 32B edges it on the weighted score. Both fit comfortably starting at RTX PRO 6000 Blackwell 96 GB; below that, only Qwen 3 32B fits.

MODEL · A★ EDGE
Qwen 3 32B
PARAMS: 32BCTX: 128KFAMILY: qwenLICENSE: commercial OK
MODEL · B
Llama 3.3 70B Instruct
PARAMS: 70BCTX: 128KFAMILY: llamaLICENSE: commercial OK

Verdict for chatPick → Qwen 3 32B

Weighted: 25% (Qwen 3 32B) vs 5% (Llama 3.3 70B Instruct)

Qwen 3 32B is the better fit for chat on the dimensions we score, taking 2 of 10 rows. The weighted score (25% 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.

WILL IT RUN — HARDWARE FIT

For each common hardware tier, the best-fitting quant for each model + predicted decode tok/s. ✓ comfortable, ~ tight, ✗ doesn't fit. tok/s extrapolated from bandwidth × active-footprint — measure on your stack.

Hardware tierQwen 3 32BLlama 3.3 70B InstructVerdict
RTX 3060 12 GB
budget consumer · 360 GB/s
✗ doesn't fit✗ doesn't fitNeither
RTX 4060 Ti 16 GB
consumer 16 GB · 288 GB/s
✗ doesn't fit✗ doesn't fitNeither
RTX 3090 24 GB
used flagship · 936 GB/s
~ Q4_K_M~22 GB · 29 tok/s est.
✗ doesn't fitOnly A
RTX 4090 24 GB
consumer flagship · 1008 GB/s
~ Q4_K_M~22 GB · 31 tok/s est.
✗ doesn't fitOnly A
RTX 5090 32 GB
next-gen flagship · 1792 GB/s
✓ Q4_K_M~22 GB · 56 tok/s est.
✗ doesn't fitOnly A
RTX PRO 6000 Blackwell 96 GB
workstation · 1792 GB/s
✓ Q8_0~37 GB · 32 tok/s est.
✓ Q6_K~64 GB · 19 tok/s est.
Both fit
Mac Studio M4 Max 128 GB
apple unified · 546 GB/s
✓ Q8_0~37 GB · 11 tok/s est.
✓ Q6_K~64 GB · 7 tok/s est.
Both fit
Mac Studio M3 Ultra 192 GB
apple unified flagship · 800 GB/s
✓ Q8_0~37 GB · 16 tok/s est.
✓ Q8_0~81 GB · 8 tok/s est.
Both fit
✓ Comfortable (≥30% headroom)~ Tight (fits, <30% headroom)✗ Doesn't fit
DIMENSION MATRIX
DimensionQwen 3 32BLlama 3.3 70B InstructEdge
Editorial rating (1-10)
Editor rating — single human assessment across reasoning, fluency, tool-use, instruction-following.
8.99.1tie
Parameters (B)
32.0B70.0BLlama
Context length (tokens)
131K131Ktie
License (commercial OK?)
✓ Apache 2.0✓ Llama 3.3 Community Licensetie
Decode tok/s on chosen hardware
— pick hardware —— pick hardware —tie
Fits on chosen hardware (Q4_K_M)
— pick hardware —— pick hardware —tie
Cost to run (local, Q4)
Smaller model → less VRAM + less electricity per token. Cross-reference with /cost-vs-cloud for $-anchored math.
18.0 GB at Q4_K_M39.4 GB at Q4_K_MQwen
Community popularity
Editorial popularity score — proxy for runtime support breadth + community recipe availability.
9293tie
Multimodal support
text onlytext onlytie
Released
2025-04-292024-12-06Qwen
DETAIL · A
Qwen 3 32B →
Editorial verdict, how to run, hardware guidance.
DETAIL · B
Llama 3.3 70B Instruct →
Editorial verdict, how to run, hardware guidance.
CURATED
Browse curated pairs →
9 head-to-head editorial pages for the highest-search-intent pairs.

Comparison data computed from live catalog rows + the model-battle comparator (src/lib/model-battle/comparator.ts) + the cross-tier fit calculator (src/lib/compare/model-hardware-fit.ts). All numbers are extrapolated from VRAM / bandwidth math; for measured runs see /benchmarks. The URL captures your selections — share it for the same view.