110 tokens per second. On a consumer GPU you can buy at Best Buy. Running a 35-billion parameter model. That's not a typo — that's the new reality of local AI inference, and it just made your cloud API bill look like a hostage note.
A community benchmark posted this week shows Qwen3.6 35B hitting 110 tok/s on an RTX 4070 Super using llama.cpp with MTP (multi-token prediction) speculative decoding. For context, that's faster than many cloud API endpoints — and it runs on hardware that costs $600.
The Problem: Cloud Inflation Is Out of Control
Enterprise AI token costs have become a genuine crisis. We're not talking about rounding errors:
- $1.3 million/month — what one organization pays to run 100 AI agents
- Microsoft pulled Claude Code licenses because the bill was too high
- Uber burned its entire 2026 AI budget by April — four months into the year
- Goldman Sachs predicts 24x token growth over the next 18 months
The pricing model is broken. You're renting intelligence by the syllable, and the landlords keep raising rent. Every time your agent thinks harder, your AWS bill twitches.
Meanwhile, consumer GPUs have gotten weirdly good at this. The RTX 4070 Super has 12GB of VRAM. With 4-bit quantization (which NVIDIA just validated at scale — 10 trillion tokens trained at 4-bit matching FP8 accuracy), a 35B model fits comfortably. With speculative decoding via MTP, you get throughput that competes with cloud.
The Solution: The Local AI Stack Has Arrived
Three things happened simultaneously that made local AI viable for production:
llama.cpp MTP support. Multi-token prediction speculative decoding delivers a 2.4x speedup for local inference. Instead of generating one token at a time, the model predicts multiple tokens and validates them in parallel. It's like reading ahead and confirming — fast.
Ollama v0.30.0 RC. Direct llama.cpp integration with Apple Silicon MLX support. One command to pull and run a model. No configuration hell. ollama run qwen3.6:35b and you're running frontier-quality inference locally.
4-bit quantization is production-ready. NVIDIA's NVFP4 paper proved that 4-bit training on 10 trillion tokens matches FP8 accuracy. If 4-bit is good enough for training, it's more than good enough for inference. That means a model that needs 70GB in FP16 fits in under 20GB — well within consumer GPU territory.
Benchmarks: Local vs. Cloud
- 110 tok/s — Qwen3.6 35B on RTX 4070 Super (4-bit quant, MTP speculative decoding)
- 2.4x speedup from MTP speculative decoding in llama.cpp
- ~$0.00/hour marginal cost after hardware purchase vs. $3-15/M tokens for cloud APIs
- 4-bit quantization matches FP8 accuracy at 2-3x throughput improvement (NVIDIA, 10T token validation)
- LM Studio MTP — same speculative decoding available in GUI for non-terminal users
- Under $2,000 total hardware cost for a local inference setup that handles most production workloads
Caveat: The 110 tok/s figure is for a specific model/config. Your mileage will vary with model size, quantization level, and workload complexity. But the trend is clear and reproducible.
Impact: The Economics Don't Lie
Here's the math that should terrify cloud AI providers.
A $600 GPU running local inference at 110 tok/s handles roughly 400 million tokens per month at continuous utilization. At Claude's API pricing, that same token volume would cost $1,200-3,000/month. The GPU pays for itself in two to three weeks.
For enterprises running AI agents 24/7 — customer support bots, internal tools, data processing pipelines — local inference isn't a cost optimization. It's a fundamental shift in the cost structure. You go from variable, unpredictable API costs to a fixed hardware investment with zero marginal cost.
The caveat: you need technical talent to set this up. Only 13% of employees are ready for agentic AI, and even fewer can configure quantized model serving. That's the real bottleneck — not the hardware.
But if you have a technical team (or work with one — and yes, that's what we do at Atobotz), the local AI stack is production-ready today. Not next quarter. Not after the next model release. Today.
The Bottom Line
The cloud AI pricing model was built on the assumption that you can't run these models yourself. That assumption is dead. 110 tok/s on consumer hardware isn't a parlor trick — it's a proof that the economics have flipped.
If you're still paying cloud prices for inference on workloads that run continuously, you're leaving six figures on the table. The hardware is ready. The software is ready. The question is whether your team is ready to make the switch.