Gemma 4 MTP vs Llama 5 vs Qwen 3.6: Speed (May 2026)

Google’s Gemma 4 Multi-Token Prediction drafters land a 3x speedup on consumer hardware without losing quality. Here’s how Gemma 4 + MTP, Llama 5, and Qwen 3.6 stack up for local LLM inference in May 2026.

Last verified: May 11, 2026

At a glance

Property	Gemma 4 + MTP	Llama 5	Qwen 3.6
Vendor	Google	Meta	Alibaba (Qwen)
License	Apache 2.0	Llama Community	Apache 2.0
Released	April 2026 (MTP added April-May 2026)	Early 2026	2026
Native MTP drafters	Yes	No	No
Speedup vs single-token decode	Up to 3x	Baseline	Baseline
Best for	Fast local chat, mobile, edge	Fine-tuning, general purpose	Coding, Chinese, agentic
Quality vs latency trade	Quality preserved at 3x speed	Quality strong, single-token speed	Quality strong on code
Quantization availability	Strong	Strongest ecosystem	Strong
Mobile/edge ready	Yes (designed for)	Partial	Partial

Multi-Token Prediction in plain English

Standard LLM inference generates one token at a time. The bottleneck on consumer hardware isn’t math — it’s memory bandwidth. Every token forces the model’s weights through the memory bus once.

Speculative decoding works around this:

1. A lightweight drafter model predicts the next several tokens fast.
2. The main model verifies those tokens in parallel — one big batched forward pass.
3. Tokens that match the main model’s distribution are accepted; the rest are discarded and regenerated.

Net result: fewer trips through memory per accepted token. On bandwidth-bound consumer hardware, this can multiply throughput.

Google’s contribution with Gemma 4’s MTP drafters:

• The drafter is trained jointly with the main Gemma 4 models, so it predicts tokens in the right distribution.
• The drafters are shipped open-source under Apache 2.0, alongside Gemma 4.
• The speedup reaches up to 3x on consumer hardware with no loss of output quality — verified by Google’s published evals.
• Particularly strong on mobile phones and gaming PCs where bandwidth is the binding constraint.

Speed: Gemma 4 + MTP wins

For pure tokens-per-second at a given quality bar, Gemma 4 with MTP is the leader on consumer hardware as of May 2026. Three-times faster is large enough that interactive workloads (chat, autocomplete, voice assistants on-device) feel meaningfully different.

Llama 5 and Qwen 3.6 are not slower per-call than older models — they just don’t ship native MTP. To get equivalent speedups, you’d need to train your own drafters, which is non-trivial and ecosystem-specific.

Quality: workload-dependent

Speed isn’t quality. Where Gemma 4 + MTP wins on speed, the others may win on raw output:

• Llama 5 has the strongest fine-tune ecosystem. For domain-specific fine-tunes (medical, legal, code-domain), the Llama 5 base often produces the best end product.
• Qwen 3.6 consistently leads on coding benchmarks among open models — better than Gemma 4 for code-generation workloads. Strong on Chinese and multilingual.
• Gemma 4 is competitive on general chat and reasoning but doesn’t lead in either category — its strength is the speed-quality balance.

Ecosystem and tooling

Capability	Gemma 4 + MTP	Llama 5	Qwen 3.6
Ollama support	✅	✅	✅
LM Studio support	✅	✅	✅
vLLM support	✅	✅	✅
llama.cpp / GGUF quantizations	✅	Best	✅
HuggingFace fine-tunes	Growing	Largest	Strong
On-device mobile	Designed for	Possible	Possible
Open weights	✅	✅	✅

For raw ecosystem breadth, Llama 5 still leads. For on-device/mobile, Gemma 4 is purpose-built. Qwen 3.6 holds the coding crown among open models.

Memory and hardware footprint

All three families ship multiple sizes:

• Gemma 4: small (mobile-ready), medium (laptop/desktop), larger sizes for workstation-class hardware.
• Llama 5: full range from compact to flagship sizes (~70B+ parameters).
• Qwen 3.6: similar range, with Qwen3-Coder variants tuned specifically for code.

On 24GB consumer GPUs, all three deliver mid-size variants at usable speeds. With Gemma 4’s MTP turned on, throughput is materially higher.

When to pick each

Pick Gemma 4 + MTP when:

• Speed on consumer hardware is the priority
• Mobile/edge deployment is the target
• General chat or reasoning is the workload
• You want strong quality without paying for top SWE-bench numbers

Pick Llama 5 when:

• You’re fine-tuning on proprietary data — the ecosystem is the largest
• Quantization quality matters (best GGUF/llama.cpp story)
• You want the broadest community of recipes, prompts, and integrations
• You need the Llama 5 brand for procurement reasons

Pick Qwen 3.6 when:

• Coding is the workload — Qwen consistently leads open coding benchmarks
• Chinese-language capability matters
• Agentic workflows with strong tool use
• You want the strongest open coder available

How to use them together

Most production teams running open LLMs in May 2026 keep multiple models around:

• Gemma 4 + MTP for fast chat and on-device assistants.
• Qwen 3.6 (or DeepSeek V4-Flash) for code generation.
• Llama 5 fine-tunes for domain-specific agents.

Routing logic — a thin model-router in front — sends each request to the cheapest model that can handle it. The “AI router” pattern that emerged in late 2025 / early 2026 makes this practical.

What to watch next

• Llama 5 MTP equivalents. Meta hasn’t shipped native MTP drafters yet — expect the open community to publish drafters trained for Llama 5.
• Qwen MTP drafters. Same — community drafters likely to follow.
• On-device benchmarks. Independent measurements of Gemma 4 + MTP on Pixel, iPhone, and gaming laptops.
• Larger Gemma 4 sizes. Currently Gemma 4 is open-mid-size — flagship sizes may follow.

---

Related reading

• What is Gemma 4
• Gemma 4 vs Qwen 3.5 vs Llama 4
• Qwen 3.6 vs DeepSeek V4 vs Llama 5 coding
• Llama 5 vs DeepSeek V4 vs Qwen 3.5 open models

Last verified: May 11, 2026 — sources: Google Gemma 4 blog, Google Multi-Token Prediction announcement, ai.google.dev MTP overview, Belitsoft Gemma 4 coverage, Reddit r/AIGuild discussion, NYU RITS analysis.