If you’ve narrowed your local-AI shortlist down to Qwen3 and Llama 3.3 70B, you’re really choosing between two different bets about hardware. Qwen3’s flagship dense model for a single consumer card is the 32B, which fits comfortably on a 24GB GPU. Llama 3.3 ships as a 70B — a genuinely capable model that needs roughly twice the memory and, for most people, a second GPU or a big-memory Mac to run well. The question “which should I run locally” is mostly the question “what card do I have, and is the 70B’s edge worth the silicon to feed it?” This page answers both, with real VRAM math, honest token-per-second figures, and where each model lands on writing, reasoning, and refusals.
Both are excellent open-weight models that run cleanly under Ollama. Neither is a wrong answer. But they suit different rigs and different priorities, and the gap between them is smaller than the 32-versus-70 number suggests.
The matchup: a 32B that fits 24GB vs a 70B that needs much more
The cleanest way to frame Qwen3 vs Llama 3.3 local is by the card each one targets.
Qwen3 32B is a dense 32-billion-parameter model built to be the strongest thing a single 24GB GPU can run at a usable quant. At Q4_K_M it lands around 18–20GB, leaving a few gigabytes for the KV cache that holds your conversation. That’s the entire pitch of the 24GB tier: a 3090 or 4090 runs a 32B-class model and finally feels like a frontier assistant rather than a toy. Qwen3 also has a hybrid “thinking” mode you can toggle for harder problems.
Llama 3.3 70B is Meta’s 70-billion-parameter instruct model — notable because it delivers quality in the neighborhood of the much larger Llama 3.1 405B for general chat and instruction-following, in a package half that size. But “half of 405B” is still 70B, and at Q4_K_M the weights alone want roughly 40GB, climbing toward 46GB+ once you add inference overhead and context. That does not fit in 24GB. Full breakdown in how much VRAM you need for a 70B model.
| Qwen3 32B | Llama 3.3 70B | |
|---|---|---|
| Parameters | 32B (dense) | 70B (dense) |
| VRAM @ Q4_K_M | ~18–20GB | ~40–46GB+ |
| Single 24GB card? | Yes, comfortably | No |
| Practical rig | One 3090/4090 | Dual 24GB, 48GB card, or 64GB+ Mac |
| Thinking mode | Yes (toggleable) | No (standard instruct) |
So this isn’t a fair fight on equal hardware. It’s a real-world choice: a model that runs now on the GPU most enthusiasts own, versus a bigger model that asks you to commit to a heavier build.
Hardware cost: what each actually requires
This is where the decision usually gets made. The models cost nothing; the silicon does.
Qwen3 32B needs a single 24GB GPU. A used RTX 3090 is the value king here, and a 4090 is the same VRAM with faster bandwidth (covered in the 24GB guide). That’s a one-card build — one PCIe slot, a normal power supply, a normal case. If you already game on a 3090/4090, you already own the rig.
Llama 3.3 70B needs roughly 40–48GB of memory to run well, which means one of three paths:
| Path | Memory | 70B @ Q4? | Relative cost | Notes |
|---|---|---|---|---|
| Dual RTX 3090 | ~48GB pooled | Yes, with context | Lowest practical | The classic enthusiast build; Ollama splits the model across both cards |
| Single 48GB workstation card | 48GB | Yes | High | Quiet, one slot, expensive |
| Mac, 64GB+ unified memory | 64–128GB | Yes | Higher | Slower tokens, near-silent, low power |
| Single RTX 5090 | 32GB | Only at aggressive Q3 | Medium | Great 32B card, marginal 70B card |
The honest summary: a single 3090 runs Qwen3 32B today, while a genuinely comfortable Llama 3.3 70B is a dual-GPU or big-Mac commitment — a second card plus the PSU and case to feed it is a real line item, detailed in our GPU-for-a-70B-at-home breakdown. If you don’t already want that hardware for other reasons, the 70B’s incremental quality rarely justifies building the rig from scratch.
Writing voice and creative quality head-to-head
For prose, the difference is one of flavor more than raw competence — both are strong.
Llama 3.3 70B tends to write with more warmth and natural rhythm out of the box. It’s looser, more conversational, and slightly better at sustaining a consistent character voice across a long exchange — the extra parameters show up as smoother, less “templated” prose. For roleplay and companion-style chat where feel matters, many people prefer it untuned.
Qwen3 32B is more precise and instruction-faithful. It does exactly what you ask, structures output cleanly, and is excellent when you want control — a specific format, a specific tone, a specific constraint held over many turns. Its default voice can read a touch more clinical than Llama’s, but it follows steering better, so a good system prompt closes most of the gap.
In practice the responsiveness difference often matters more than the prose difference. A snappy 32B that answers instantly can feel more alive in a back-and-forth than a 70B that pauses to think between sentences — a recurring theme in our local-AI creative writing and roleplay model notes. Neither model is the best pure-creative pick in 2026 (purpose-tuned writing models often beat both), but as all-rounders, Llama edges warmth and Qwen edges control.
Reasoning, coding, and multilingual differences
Here the two diverge more sharply, and it favors Qwen on several axes.
- Reasoning: Qwen3 32B’s toggleable thinking mode is a real advantage on math, logic, and multi-step problems — it can emit a chain of intermediate steps before answering. Llama 3.3 70B is a standard instruct model without a built-in reasoning mode; it’s strong but solves problems in one pass. For hard reasoning on a budget, the 32B with thinking on is often the smarter pick despite being half the size.
- Coding: Both write decent code, but neither is the local coding champion — that title belongs to dedicated coder models. If coding is your main use, a Qwen2.5-Coder-32B fits the same 24GB card and beats both generalists at autocomplete and refactoring (see best local coding model by VRAM). Between these two specifically, Qwen3 has a slight edge on structured coding tasks.
- Multilingual: Qwen3 has notably broad and strong multilingual coverage, especially across Asian languages, where it’s one of the best open-weight options. Llama 3.3 is solid across major European languages but generally narrower. If you work outside English, Qwen is the safer bet.
The pattern: Llama 3.3 70B wins on raw general-knowledge breadth and natural English feel; Qwen3 32B wins on structured reasoning, multilingual range, and doing-what-it’s-told — at a fraction of the hardware.
Refusal behavior and steerability for uncensored use
Both are aligned, safety-tuned instruct models out of the box, and both will refuse some requests by default. This is a property of the base release, not a flaw — and it’s exactly why the local community produces fine-tunes.
In broad community experience, Llama 3.3 tends to be the more cautious of the two in stock form, with Meta’s alignment producing more frequent refusals and disclaimers on edgy-but-legal prompts. Qwen3 is often reported as somewhat more willing to engage, though it carries its own refusal patterns. Treat both stock models as steerable but guarded.
The real unlock for uncensored use isn’t the base model — it’s the fine-tune. Both families have:
- Abliterated variants, where the refusal direction is surgically removed from the weights (explained in abliterated models, explained).
- Community fine-tunes retrained for open, in-character behavior.
Crucially, the 24GB-vs-48GB hardware reality follows the fine-tune too. An abliterated Qwen3 32B runs on your single 3090; an abliterated Llama 3.3 70B still needs the dual-GPU rig. For most people who want an uncensored local model that actually runs, a steered 32B is the path of least resistance — see best uncensored local AI models and why this is a local-only freedom in why cloud AI censors you.
Speed: real tok/s on common hardware
Token generation is memory-bandwidth bound, so a model that fits entirely in VRAM is fast, and one that spills to system RAM crawls. This is where the matchup gets lopsided.
| Setup | Model | Rough tok/s | Feel |
|---|---|---|---|
| RTX 3090 (24GB) | Qwen3 32B Q4 | ~25–35 | Readable, you watch it think |
| RTX 4090 (24GB) | Qwen3 32B Q4 | ~35–50 | Snappy |
| Dual RTX 3090 (48GB) | Llama 3.3 70B Q4 | ~15–20 | Usable, deliberate |
| Mac 64GB+ unified | Llama 3.3 70B Q4 | ~8–12 | Slow but quiet |
| Single 24GB + heavy offload | Llama 3.3 70B Q4 | low single digits | Slideshow — avoid |
The headline: Qwen3 32B on one card is meaningfully faster than Llama 3.3 70B on two. A bigger model fully in VRAM still generates each token through more layers, so even a well-fed 70B is slower per token than a 32B. And if you try to run the 70B on a single 24GB card by offloading the overflow to CPU, you fall off the speed cliff — a few tokens per second, fine for batch jobs, painful for chat. For what counts as usable, see tokens per second, explained. If you only own one 24GB card, the 32B isn’t just the model that fits — it’s the model that’s pleasant.
The decision tree: pick by the GPU you have
Cut through everything above with the card in your machine.
- 8–16GB VRAM: Neither. You’re below this matchup entirely — run an 8B–14B model and check the 8GB or 12/16GB guides. Qwen3 ships smaller sizes that fit here.
- One 24GB card (3090/4090): Qwen3 32B. It’s the right model for your hardware, full stop — fast, capable, and it leaves room for context. Don’t try to cram a 70B; you’ll hate the offload speed.
- Dual 24GB, a 48GB card, or 64GB+ Mac: Now Llama 3.3 70B is on the table. Run it if you want maximum general capability and natural prose and you’ve already paid for the memory. Many owners keep both pulled and switch by task.
- No GPU / want it now: Don’t build a rig on a whim — see the hosted path at the end.
- Buying hardware specifically for this: A used 3090 + Qwen3 32B is the best value in local AI. Only step up to a dual-3090 70B rig if the 70B’s edge genuinely matters to your work; for most chat, companion, and reasoning use, it won’t move the needle enough to justify the second card.
Verdict + the uncensored angle for each
For most people, run Qwen3 32B. It fits the GPU enthusiasts actually own, runs fast on a single card, reasons better thanks to thinking mode, and has stronger multilingual range. The full writeup is in our Qwen3 32B review.
Run Llama 3.3 70B only if you already have 40GB+ of memory and you specifically want its warmer prose and broader general knowledge. It’s a genuinely better model in absolute terms — but the absolute gap over a 32B is narrow, and the hardware gap is wide.
On the uncensored question, the calculus is the same for both: the stock models are aligned and will refuse some prompts, and the freedom comes from abliterated or community fine-tunes — which exist for both families. The deciding factor is, again, the rig. An uncensored Qwen3 32B runs on your one card today; an uncensored Llama 3.3 70B still wants the dual-GPU build. Either way, running it locally means no logging, no cloud, no monthly bill, and no model deciding for you what you’re allowed to ask — the whole reason to self-host instead of renting someone else’s GPU.
If you’ve settled on a 24GB card and a steerable model like Qwen3 32B, the missing piece is the experience wrapped around it — and that’s what Ember is: a one-time-purchase, uncensored AI companion that runs 100% on your own Ollama models, built for exactly the 3090/4090 owner who already has the VRAM and wants real ownership rather than a subscription.