TL;DR: Meta Avocado is delayed to May 2026, reportedly underperforms competitors, and may ship as a proprietary model — meaning local inference isn't even on the table. Qwen 2.5 72B and Llama 3.3 70B are production-ready today. Build the right hardware and deploy this week.
There's a detail buried in the Avocado delay coverage that most people are glossing over. Every headline frames this as a timing story — Meta pushed back the launch, community mocks the schedule, end of article. But the actual problem runs deeper, and it directly affects every power user waiting on the sidelines.
Meta is reportedly developing Avocado as a proprietary model. Not open weights. Not a Llama-style release you can pull from Hugging Face. A closed model — the same category as GPT-4o and Claude.
If that holds, the question "should I wait for Avocado?" doesn't just have a bad answer. It has the wrong premise entirely.
Meta's Avocado: Announcement, Delay, and What Changed
Meta originally targeted Avocado for late 2025, slipped to Q1 2026, and now the confirmed date is May 2026. The stated reason matters: internal performance evaluations showed Avocado falling short. It reportedly sits between Google Gemini 2.5 and Gemini 3.0 on reasoning, coding, and writing — which is not the position you want when justifying a flagship release.
Meta reportedly even considered temporarily licensing Google's Gemini technology to bridge the gap while Avocado was improved. That's not a rumor about a launch sliding a few weeks — that's a signal the model has structural problems.
Warning
Unlike Meta's Llama series, Avocado is expected to launch as a proprietary model. If correct, power users cannot download, self-host, or run Avocado locally regardless of when it ships. You're waiting for something that may never be available for local inference.
Why does this distinction matter for hardware builders? Because the Llama releases were valuable precisely because you could run them on your own silicon, on your own terms. Avocado, if closed, competes with OpenAI and Anthropic — not with the open-source stack you're actually building on.
Why Power Users Should Care About This Specifically
Every month of waiting is production value lost. If your workflows involve on-premise deployment, data privacy requirements, or offline operation, a proprietary model fails the minimum requirements before you even evaluate performance. And if the model is open — if Meta reverses course — the community will have had 6+ additional months to stress-test the alternatives.
Either way, you don't wait.
How the Open-Source Community Already Won This Race
While Meta delayed Avocado, the open-source community kept shipping. Two models stand out for production deployment in 2026.
Qwen 2.5 72B (released September 2024) leads on coding benchmarks and is the current MMLU front-runner in its class. The model has been in production environments for over 18 months — you're not deploying an experimental release.
Llama 3.3 70B (released December 2024) improved meaningfully over Llama 3.1 on instruction following and multilingual tasks, while preserving the community infrastructure that makes it the most fine-tuned model in the open-source ecosystem. More tooling, more documented failure modes, more community support for edge cases.
Best Use Case
Coding, benchmarks, MMLU-heavy tasks
Fine-tuning, large community ecosystem
Note
Mistral Large 2 is a 123B parameter model — not comparable hardware territory. It requires ~73 GB VRAM at Q4_K_M, which means 4+ datacenter-grade or professional GPUs. If you're shopping for a consumer 70B-tier model, Qwen and Llama are your two options. Mistral Small 3.1 is the lightweight alternative worth considering for lower-VRAM builds.
Qwen 2.5 vs. Llama 3.3: Which to Deploy Now
The answer is almost entirely use-case driven, because the hardware requirements are nearly identical.
Pick Qwen 2.5 72B if: your primary workload is code generation, reasoning tasks, or structured output. It benchmarks consistently higher on HumanEval and MATH.
Pick Llama 3.3 70B if: you need fine-tuning, custom adapters, or integration with existing tooling that was built around the Llama architecture. The ecosystem advantage is real — there are far more documented examples, failure modes, and optimization guides.
Both are production-ready. Both have been running in enterprise environments for over 12 months. Neither is a gamble.
The Hardware to Build RIGHT NOW
Here's the reality nobody in the Avocado hype cycle is talking about: 70B models require 40–43 GB of VRAM at Q4_K_M quantization. A single RTX 5090 (32 GB) doesn't hold it. An RTX 5070 Ti (16 GB) isn't even in the conversation.
The VRAM math is non-negotiable. You need a rig that can actually load the model before you benchmark anything.
Entry: Two Used RTX 3090s (~$1,900 total)
Two RTX 3090s (24 GB each) give you 48 GB combined — the 70B Q4_K_M fits cleanly with headroom. Used RTX 3090s currently trade around $600–650 each on the secondary market (as of March 2026). Add a capable workstation base — decent Threadripper or Xeon, 64 GB system RAM, PCIe 4.0 motherboard — and you're around $1,900 total.
What you get: Qwen 2.5 72B Q4_K_M at roughly 15–20 tok/s. Llama 3.3 70B similar. Usable for real production workflows.
Trade-off: PCIe-limited inter-GPU bandwidth (no NVLink on any consumer card since the 30 series). Works fine for inference; fine-tuning across two cards is more complicated.
Mid-Range: Mac Studio M3 Ultra 96 GB ($3,999)
This is the argument that frustrates NVIDIA purists: the Mac Studio M3 Ultra starts at $3,999 and comes with 96 GB of unified memory. Qwen 2.5 72B and Llama 3.3 70B Q4_K_M both fit entirely in unified memory, no splitting required.
What you get: 12–18 tok/s for 70B models, verified benchmarks as of March 2026. Silent operation. No multi-GPU complexity. The machine doubles as a full development workstation.
Trade-off: You're locked into Apple Silicon and MLX/Ollama for inference. If your stack requires CUDA-specific tooling or vLLM extensions, this doesn't fit.
For anyone who doesn't have a specific CUDA dependency, the Mac Studio is arguably the best value entry point to serious 70B inference in 2026. Check our guide to local LLM inference performance metrics for the full benchmark methodology behind these numbers.
Power User: Dual RTX 5090 (~$9,100+ total)
Two RTX 5090s at March 2026 street prices (~$3,800 per card) plus a high-end workstation base lands around $9,100–$9,500 total. No NVLink — NVIDIA removed consumer NVLink with the RTX 40 series and it was not reinstated on RTX 50 (Blackwell). The cards communicate over PCIe 5.0 x16 instead.
What you get: ~27 tok/s on Qwen 2.5 72B and Llama 3.3 70B via Ollama 0.6.5, verified benchmarks from Databasemart (March 2026). 64 GB combined VRAM means all current 70B models load without spillover.
Trade-off: RTX 5090s are currently trading at 75–90% above MSRP due to global memory shortages. The $9,000+ build cost for ~27 tok/s is a harder sell when a $3,999 Mac Studio gets you 12–18 tok/s.
Best For
Budget CUDA builds
All-in-one, no CUDA dep
Max throughput, CUDA All prices as of March 2026. GPU street prices — not MSRP — used for dual RTX 5090 calculation.
For a deeper look at deploying Qwen on these hardware tiers, see our Qwen 2.5 local deployment guide.
Why Starting Today Beats Waiting for May
The math isn't complicated. At 20 tok/s continuous, a dual RTX 3090 build processes approximately 155 million tokens in 90 days. That's real workloads processed, ROI demonstrated to stakeholders, and failure modes documented — before Avocado ships.
By the time May 2026 arrives, your deployment will have 90 days of production data. Migrating to Avocado — if it's actually open-source and actually better — takes one day on the same hardware. You lose nothing by starting now. You lose three months by waiting.
Why Meta's Delays Are Actually Good for Open-Source
This is consistent with how open-source AI development works. When a commercial lab delays or closes a model, the gap creates incentive. Qwen 2.5 released into exactly the space where Llama 3.1 left room for improvement. Llama 3.3 iterated based on real production feedback that 3.1 accumulated over months.
Tip
Open-source models don't wait for announcements — they iterate on deployment data. Qwen 2.5 72B and Llama 3.3 70B will have 6+ months of additional community hardening by the time Avocado ships in May. Models gain robustness from real-world usage patterns in a way no internal evaluation catches.
There's also a hardware pricing dimension. GPU prices tend to spike on hype cycles — whenever a new model is announced or a major benchmark drops, demand for high-VRAM cards follows. Periods of announcement without delivery (like right now, during the Avocado delay) are when you actually find RTX 5090s at the low end of their market range and used RTX 3090 prices sit calmly on Swappa.
The community isn't standing still either. Both Qwen and the Llama fine-tuning ecosystem are releasing regular updates. By May, they'll be measurably further ahead than they are today. Avocado will be entering a stronger field, not a weaker one.
The Math: Should You Wait or Start Building Now?
Wait if: Meta publicly announces a specific, meaningful Avocado advantage — 30%+ speed improvement on the same hardware, a reasoning breakthrough with verifiable independent benchmarks, or confirmed open-source weights available for local inference. None of these conditions currently exist.
Build now if: you have inference workloads in the next three months. Which, if you're a power user, you do.
The hardware hedge matters here. Model inference is portable. You're not buying a Qwen machine or a Llama machine — you're buying a VRAM budget. When something genuinely better ships, you swap the model file and restart Ollama. The build doesn't change.
For a side-by-side performance comparison of all three models mentioned, see our Qwen vs. Llama vs. Mistral comparison.
What Avocado Needs to Make You Care About the Wait
Avocado has to clear a high bar to justify the attention it's gotten. What would actually matter:
Confirmed open weights for local inference. If Avocado ships proprietary, the conversation ends for any power user who needs on-premise deployment. This isn't a nice-to-have — it's a category requirement.
30%+ throughput improvement on equivalent hardware vs. Qwen 2.5 72B. Anything less than that rounds to noise at production scale.
A verifiable reasoning breakthrough on MATH, multi-step coding, or long-chain instruction following — independently benchmarked, not Meta's internal evaluations.
A credible, specific release timeline. "May 2026" is better than "Q2/Q3," but Meta has already moved this date twice. The burden of proof is on them.
The CraftRigs Take
Deploy Qwen 2.5 72B or Llama 3.3 70B today on the hardware tier that fits your budget. If Avocado arrives open-source, benchmarks ahead of what you're running, and ships on the stated timeline — you migrate in a day and lose nothing. If it doesn't, you've been running production workloads for three months while everyone else was waiting for a model that may never be downloadable.
The open-source community didn't pause for this announcement. You shouldn't either.
FAQ
What is Meta Avocado and when is it releasing?
Meta Avocado is Meta's next-generation AI model, originally targeting late 2025, delayed to Q1 2026, and now pushed to May 2026. The delay stems from performance gaps — internal evaluations reportedly placed it between Google Gemini 2.5 and Gemini 3.0, short of expectations. Unlike Meta's Llama series, Avocado is expected to launch as a proprietary closed model, which means it won't be available for local inference regardless of when it ships.
Can you run Qwen 2.5 72B on a single consumer GPU?
No. Qwen 2.5 72B at Q4_K_M quantization requires approximately 40–43 GB of VRAM. A single RTX 5090 at 32 GB is insufficient. Practical options: two RTX 3090s (48 GB combined), two RTX 5090s (64 GB combined), or Apple Silicon with 96 GB+ unified memory such as the Mac Studio M3 Ultra. Using a single 24 GB card with CPU spillover drops throughput to ~2–5 tok/s — not usable for production work.
What's the best hardware to run 70B models locally in 2026?
The Mac Studio M3 Ultra 96 GB ($3,999) is the simplest all-in-one path — 12–18 tok/s on Llama 3.3 70B and Qwen 2.5 72B at Q4, silent, no multi-GPU configuration overhead. For maximum CUDA throughput, dual RTX 5090 hits ~27 tok/s but costs $9,000+ at March 2026 street prices. Budget builders should target two used RTX 3090s at ~$600–650 each, giving 48 GB combined VRAM for 15–20 tok/s at roughly $1,900 total.
Is Llama 3.3 70B or Qwen 2.5 72B better for local deployment?
They require identical hardware and deliver similar performance in most categories. Qwen 2.5 72B benchmarks higher on coding and MMLU tasks (released September 2024). Llama 3.3 70B (released December 2024) has the larger community ecosystem — more fine-tuning guides, adapter libraries, and documented production deployments. Coding-heavy workloads: Qwen. Everything else, especially anything that involves fine-tuning: Llama.
Does Avocado matter for local LLM builders if it's proprietary?
Almost certainly not. A closed proprietary model cannot be downloaded, self-hosted, or run offline — which rules it out for any power user with data privacy requirements, on-premise deployment mandates, or offline operation needs. Even if Avocado performs well on benchmarks, the access model makes it irrelevant to the local inference use case entirely.