Ryzen AI Max+ 395 128GB vs RTX 3060 12GB for Local LLMs

If you are choosing between a Ryzen AI Max+ 395 mini-PC with 128GB of unified memory and a desktop with a single RTX 3060 12GB for running local LLMs, the answer is: it depends on model size. The 395 lets you load 32B and 70B models that simply will not fit on a 12GB card, but the 3060's GDDR6 bandwidth wins on the 7B-14B models most hobbyists actually run day to day.

The unified-memory vs dedicated-VRAM tradeoff for hobbyist inference rigs

Local LLM hardware in 2026 splits along one clean line: how big a model do you want to run? An RTX 3060 12GB costs about $400 and gives you 360 GB/s of GDDR6 bandwidth tied to a fast CUDA core count. A Ryzen AI Max+ 395 mini-PC with 128GB of LPDDR5X-8000 unified memory costs around $1,800 to $2,200 fully built and gives you 256 GB/s of system-pool bandwidth that the CPU, integrated GPU, and NPU all share.

The 3060 lives in the sweet spot for the 7B and 14B class of models. A Q4_K_M quant of Llama 3.1 8B fits in about 5.5GB, leaving headroom for context. Tokens per second on a stock 3060 in llama.cpp commonly land in the 55 to 75 range for these small models, and the Ollama default offload-all-layers behavior keeps everything on-card.

The 395 is the only mainstream non-server platform that can resident-load a 70B Q4 model without offloading half of it to disk. Once you cross that threshold, the comparison stops being about tok/s and starts being about whether the model loads at all. A 70B Q4_K_M model needs roughly 40GB to 42GB of weights plus another 6GB to 12GB for KV cache at typical context lengths. On a 3060 you have 12GB, so most of the model spills to system RAM and per-token speed collapses to single digits.

That is the headline tradeoff. Capacity wins where the model is too big for VRAM. Bandwidth wins where the model fits in VRAM with room to spare. The middle ground — 27B to 32B — is where both platforms are uncomfortable and where the choice gets interesting.

Key takeaways

• The 3060 12GB stays competitive for 7B to 14B Q4 inference and is the cheapest CUDA-supported on-ramp to Ollama and vLLM.
• The Ryzen AI Max+ 395 with 128GB of unified memory can load 32B and 70B models that physically will not fit on a 12GB consumer GPU.
• For small models, expect the 3060 to outpace the 395 by roughly 1.4x to 2.2x in pure generation tok/s, even with the 395's higher-bandwidth pool.
• For large models, the 3060's offload penalty is severe — tok/s drops 80% or more — while the 395 keeps weights resident and generation steady.
• Per-watt the 395 mini-PC wins handily; per-dollar at small models the 3060 still leads.
• CUDA support remains the 3060's other advantage: ComfyUI, vLLM, fine-tunes, and most quantization toolchains assume CUDA-first.

What does 128GB of unified memory actually let you run that 12GB of VRAM can't?

It lets you run the entire 70B-class open-weight ecosystem in a single resident load without offload. Llama 3.3 70B Q4_K_M lands at roughly 41GB plus context. Mixtral 8x7B Q5_K_M takes about 33GB. DeepSeek-V2.5 at Q4 fits in around 50GB. The Ryzen AI Max+ 395 accepts all of those plus a sensible context buffer and still has headroom for whatever else is running on the host.

The 3060 cannot. At 12GB you are capped at about a 13B model in full Q5, or a 14B at Q4, before context, scratch, and KV cache start fighting for space. You can run bigger models on a 3060 via partial CPU offload, but the moment any portion of the model crosses the PCIe bus on a per-token basis your throughput is gated by system RAM speed, not GPU bandwidth, and you will see 2 to 5 tok/s on a 32B model that an unconstrained 3060 would push at 35-plus on the small variant.

There are workflows where this matters and workflows where it does not. If you only need a fast 8B model for inline code completion or chat, you will never feel the cap. If you want to compare a 70B base model to a 70B instruct fine-tune on your own prompts, or run a long-context summarizer over a 200-page document, the 12GB ceiling is a hard wall.

How fast is the Ryzen AI Max+ 395 vs the RTX 3060 12GB in tok/s?

The numbers below are typical for llama.cpp builds and ROCm 6.x on the 395's Radeon 8050S iGPU, with Q4_K_M quantization and a 4K context window. Treat them as a floor — community tuning, draft-token speculative decoding, and newer kernel patches all push these up.

The pattern is consistent: at small models the 3060 is roughly 2x faster per token; at 32B you cross the offload boundary and the 395 pulls ahead by a similar factor; at 70B the 3060 is unusable in practice while the 395 is slow-but-real.

Spec delta at a glance

The card is the cheaper compute. The mini-PC is the cheaper capacity. Neither one is a free lunch.

Quantization matrix: what fits where

Quantization changes the picture more than any other knob. The columns below are weight-only memory needs for a roughly 8B parameter model and a 70B parameter model, before context and KV cache.

The 395 only barely accommodates a Q8 70B at 75GB plus context; FP16 70B at 140GB is beyond both. For full BF16 70B inference you are looking at workstation cards or multi-GPU rigs, which is outside both platforms' scope.

Prefill vs generation: where each platform wins

The two phases of an LLM call behave very differently on these two systems.

Prefill — encoding your prompt into KV cache — is compute-bound on a GPU. The 3060 chews through prompt tokens at hundreds per second on small models because it can saturate its CUDA cores in parallel matrix multiplies. The 395's iGPU has less peak compute, and on long prompts you will see the prefill phase pull ahead on the 3060 even when generation later swings back to the 395.

Generation — emitting one token at a time — is bandwidth-bound. Each new token reads the model weights once through memory. The 3060's 360 GB/s on GDDR6 is enough to push 60-plus tok/s on an 8B Q4 model. The 395's 256 GB/s pool feeds both the iGPU and any background work, so its effective generation bandwidth lands lower and it produces fewer tokens per second on the same model.

The practical implication: if you mostly run long-prompt, short-response RAG queries, the 3060 finishes faster on workloads it can fit. If you run short-prompt, long-generation creative writing or agent loops, the comparison narrows.

Context-length impact

A 64K context window changes the math because the KV cache grows linearly with sequence length. For a 14B Q4 model, KV cache at 64K with default fp16 KV adds roughly 7GB on top of the 8GB weights. On a 3060 that puts you right at the edge of the 12GB pool, and you will need to quantize the KV cache to 8-bit or 4-bit to fit. On the 395 the same 14B Q4 plus 64K KV totals around 15GB out of 128GB — a non-issue.

For a 70B model at 64K context, KV cache alone can hit 30GB to 40GB depending on attention layout. Again, the 3060 is simply not in the conversation. The 395 absorbs it inside its 128GB pool.

Perf-per-dollar and perf-per-watt math

Take a typical 8B Q4 workload. The 3060 at 62 tok/s for $420 yields about 0.15 tok/s per dollar. The 395 at 30 tok/s for $1,950 yields about 0.015 tok/s per dollar — an order of magnitude worse for that specific workload. If 8B is all you need, the 3060 is the obvious value pick and you can build the rest of the desktop around an AMD Ryzen 7 5700X and a 32GB DDR4-3600 kit for about $1,000 total.

Now consider 70B Q4. The 3060 effectively cannot run it. The 395 at 6.5 tok/s for $1,950 is the only platform option under $3,000 that ships it as a single-box solution. Per-watt the 395 also wins: a 75W to 110W package replaces a 170W GPU plus 100W CPU plus board overhead, with the whole mini-PC drawing under 200W at full tilt.

Verdict matrix

Get the Ryzen AI Max+ 395 mini-PC if you want to host 32B-and-up local models, you value silence and low idle draw, you do not need CUDA-specific tooling, and you can stomach the higher upfront cost.

Get the RTX 3060 12GB build if you mostly run 7B to 14B models, you want CUDA support for fine-tuning or for stable-diffusion / ComfyUI on the same box, you already own a tower, or you are budget-constrained and want the fastest small-model performance per dollar.

Get both if you are running an agent loop that hands off small-model work to a fast local model and only escalates to the 70B model for tool planning and synthesis — a setup that mirrors the agent-PC reference architectures we cover separately.

Bottom line

Memory bandwidth makes the RTX 3060 the better small-model rig and memory capacity makes the Ryzen AI Max+ 395 the better large-model rig. There is no single winner because the question is not really 3060 vs 395 — it is which models you want to run. If your workload lives below 14B you buy the GPU. If your workload lives above 32B you buy the mini-PC. If you are not sure, build the 3060 box first and rent 70B inference for the rare occasions you need it; you can always add the 395 later as a dedicated big-model server on the same network.

Related guides

• GPT-5.5 Instant vs Local LLMs on RTX 3060 12GB
• RX 9070 XT vs RTX 3060 12GB for Local LLM Inference
• Ryzen AI Max Gorgon Halo 192GB vs RTX 3060 12GB
• Local Coding Agent on RTX 3060 12GB
• Cut AI API Bills with Local LLM on RTX 3060 12GB

Citations and sources

• AMD Ryzen AI Max product page — official spec sheet for the AI Max+ 395, including unified-memory bandwidth and NPU performance.
• TechPowerUp GeForce RTX 3060 spec page — bandwidth, compute, and reference design data for the 3060 12GB.
• llama.cpp discussions — community benchmarks and quantization recipes referenced throughout, plus the canonical Q4_K_M definition.