RTX 3060 12GB vs RX 6700 XT 12GB for Phi-4 14B Under $300 (2026)

Step 0: is your bottleneck VRAM, bandwidth, or the software stack?

Before either card is named, work out which of three limits you are actually hitting. They fail in a specific order, and buying against the wrong one wastes money.

VRAM ceiling. This is binary. Either the weights plus the KV cache fit on the card or they do not. When they do not, the runtime moves layers to system RAM and generation speed collapses by more than half. No amount of bandwidth rescues an over-subscribed card. At 12 GB, a 14B model at four-bit fits and a 27B model does not — that is the whole reason the 12 GB tier exists as a category.

Memory bandwidth. Once the model is resident, token generation is a memory-bound loop: every token reads the entire weight set once. A card with more GB/s generates more tokens per second, all else equal. This is where the RX 6700 XT should win — it carries 384.0 GB/s against the 3060's 360.0 GB/s, per TechPowerUp's RX 6700 XT database entry and its RTX 3060 12 GB entry.

Software stack. This is the one that decides the comparison, and the one nobody prices in. llama.cpp, Ollama, vLLM and ExLlamaV2 were all built against CUDA first. The ROCm and Vulkan paths arrived later, carry more version pinning, and leave measurable throughput on the table. When a card with 7% more bandwidth loses a 14B generation test by 40%, the gap is not silicon.

If your answer to Step 0 is "VRAM," both cards are equal and you should optimise for price. If it is "bandwidth," the Radeon should win. If it is "software stack" — and for a 14B local assistant in 2026 it is — the NVIDIA card wins, and the rest of this piece is about how much by.

Key takeaways

• Phi-4 14B at Q4_K_M sits in 9.5 GB of VRAM on a 12 GB card and generates 24.6 tok/s, per llmrun.dev's RTX 3060 12GB page — fully resident, with roughly 2.5 GB left for context.
• On matched Ollama runs of a 14B Q4_K_M model, LocalScore records 26.4 tok/s generation and 753 tok/s prefill on the RTX 3060 against 15.7 tok/s and 316 tok/s on the RX 6700 XT — a 68% generation gap and a 2.4× prefill gap.
• The Radeon has more memory bandwidth (384.0 GB/s vs 360.0 GB/s, per TechPowerUp) and still loses. The deficit is runtime maturity, not hardware.
• On a raw llama.cpp 7B Q4_0 test where both cards use the same code path, they are close on generation — 83.88 tok/s (6700 XT) vs 80.59 tok/s (3060) — but the 3060 nearly doubles prefill at 2,012.88 vs 1,051.20 tok/s, per llama.cpp discussion #10879.
• The 3060 draws 170 W to the 6700 XT's 230 W (TechPowerUp), so the NVIDIA card wins performance-per-watt by roughly 2.3× on 14B generation.
• Neither card is a 27B or 32B machine. Above 14B at four-bit, 12 GB runs out before compute does.

Spec delta: RTX 3060 12GB vs RX 6700 XT 12GB

The line that matters is the last one. Everything above it says these are peer cards. The RTX 3060 12 GB and the RX 6700 XT are within 7% on bandwidth and both sit on a 192-bit bus. The Radeon even carries 96 MB of Infinity Cache and beats the 3060 in most 2021-era gaming reviews — KitGuru measured 86 fps at 1080p Ultra in Cyberpunk 2077 on the 6700 XT against the 72 fps Gamersleaguepros records for the 3060 at the same settings.

For gaming, that ordering holds. For inference, it reverses.

Does Phi-4 14B fit in 12 GB of VRAM?

Yes at four-bit, with real headroom. Microsoft's Phi-4 model card puts the model at 14.7B parameters with a 16K context window. The table below derives each quantization's weight footprint from that parameter count at the nominal bits-per-weight of each GGUF scheme; the measured column is the one that decides purchases.

The measured row is the anchor: llmrun.dev's RTX 3060 12GB page records phi4:14b at Q4_K_M occupying 9.5 GB and generating 24.6 tok/s under Ollama. That leaves roughly 2.5 GB for the KV cache, the CUDA context, and whatever your desktop compositor is holding. It is enough for a working context in the 8K range and not much more.

The same page measures two useful neighbours on the same card: mistral-nemo:12b at Q4_K_M in 8.1 GB at 29.0 tok/s, and llama2:13b at Q4_K_M in 8.6 GB at 27.2 tok/s. Phi-4 is the heaviest of the three and the slowest, which is what you would expect from 14.7B parameters against 12B and 13B.

Q5_K_M is where the "just use a better quant" instinct breaks. The derived 10.5 GB leaves under 1.5 GB of headroom, which a few thousand tokens of context will eat. On a 12 GB card, Q4_K_M is not a compromise — it is the configuration.

How fast is Phi-4 14B on each card?

No single published run puts Phi-4 on both cards under identical conditions. What does exist is a matched pair of 14B-class Q4_K_M Ollama runs on LocalScore, plus a matched llama.cpp 7B run in discussion #10879 where both cards execute the same benchmark harness. Read together they bracket the answer.

Two things fall out. First, on the one test where both cards run the same llama.cpp path with the same quant, the Radeon actually wins generation — 83.88 against 80.59 tok/s — which is exactly what its bandwidth advantage predicts. The hardware is not the problem.

Second, everywhere the runtime is Ollama with a K-quant, the 3060 pulls away by a factor that no bandwidth number explains. LocalScore's RX 6700 XT results put the card at 15.7 tok/s on 14B Q4_K_M and 16.4 tok/s on an 8B model — a 1B model at 64.3 tok/s shows the card is not broken, just badly served by the K-quant kernels on that path.

For Phi-4 specifically, the honest read is: expect roughly 24-27 tok/s on the RTX 3060 — llmrun.dev's 24.6, bracketed by tyolab's 33.4 tok/s on Qwen3 14B at the same quant and LocalScore's 26.4 on Qwen2.5 14B — and roughly 15-17 tok/s on the RX 6700 XT under an equivalent Ollama setup.

Twenty-five tokens per second reads faster than most people, so it is comfortable. Sixteen is the speed where you start watching the cursor.

Why does the RX 6700 XT need a different software stack?

The 6700 XT is a gfx1031 part. AMD's ROCm system-requirements page lists which GPUs carry official support, and the consumer RDNA 2 mid-range has historically not been on it — builders run it anyway by setting an architecture-override environment variable, which works but is not a supported configuration.

That leaves three practical paths, in descending order of hassle:

1. Vulkan via llama.cpp. No ROCm install, works identically on Windows and Linux, and it is what most 6700 XT owners actually use. Build instructions are in llama.cpp's build docs. Throughput is the LocalScore column above.
2. ROCm with the architecture override. Faster than Vulkan when it works. Requires pinning kernel-module, ROCm and Python-wheel versions together, and re-checking them on every upgrade.
3. vLLM. vLLM's GPU installation guide documents a ROCm path, but it targets supported accelerators. On a gfx1031 card this is an evening of work with no guarantee.

On the NVIDIA side, the equivalent list is one item: install the driver, run Ollama. That difference is worth pricing. If your hourly rate is anything at all, an evening of stack-fighting is a meaningful fraction of the price gap between these two cards on the used market.

None of this is a claim that ROCm is bad. On officially supported hardware it is a genuine alternative. The RX 6700 XT is simply not that hardware.

What happens when context grows past 8K?

KV-cache growth is linear in context length and it comes out of the same 12 GB the weights already occupy. With Phi-4 at Q4_K_M using 9.5 GB, you have roughly 2.5 GB of runway. That is a working context in the 8K-16K range depending on cache precision — and Phi-4's window tops out at 16K anyway, per Microsoft's model card.

The measured falloff on a 12 GB RTX 3060 is well documented for 14B-class models. Hardware Corner's RTX 3060 12GB benchmarks record Qwen3 8B at Q4_K_XL running 55.2 tok/s generation with 1,696.8 tok/s prefill at 4K context, using 6.0 GB. The same model on the same card at 16,384 context drops to 41.97 tok/s generation and 1,119.23 tok/s prefill — a 24% generation loss and a 34% prefill loss from context alone, on a model small enough that nothing spilled.

The pattern repeats on 14B. tyolab measures Qwen3 14B at Q4_K_M hitting 33.4 tok/s at 4K context; singhajit.com records the same class of model at 16K context down to 22.7 tok/s, and DeepSeek-R1-Distill-Qwen-14B at Q4_K_M with a 16,384 context at 29.4 tok/s.

Two mitigations actually work. KV-cache quantization to eight-bit roughly halves the cache footprint and buys back most of the headroom at negligible quality cost. Dropping to Q3_K_M frees about 1.7 GB of weights for cache, which is the right trade if long context matters more than answer quality. What does not work is hoping: once a layer moves to system RAM, generation falls off a cliff and the card's bandwidth stops being the limit.

Prefill vs generation: which card wins which half of the request?

Every request has two phases with opposite bottlenecks, and the two cards are not symmetric across them.

Prefill — processing your prompt — is compute-bound and parallel. It scales with shading units. The RTX 3060 has 3,584 to the 6700 XT's 2,560, per TechPowerUp, and the measured gap is larger than that ratio: 2,012.88 vs 1,051.20 tok/s on the matched llama.cpp 7B run in discussion #10879, and 753 vs 316 tok/s on the LocalScore 14B runs.

Generation — producing the answer one token at a time — is memory-bound. It scales with bandwidth, where the Radeon leads on paper and wins in practice when the code path is identical (83.88 vs 80.59 tok/s on the same 7B test).

Which half you care about depends on what you do. Pasting a long document and asking one question is prefill-dominated: the 3060's 2.4× advantage there is the entire user experience. A long back-and-forth chat with short turns is generation-dominated, and on a matched runtime the cards would be close.

For a Phi-4 assistant with any retrieval or document-paste workflow, prefill dominates, and the 3060 wins that half decisively even before the Ollama K-quant gap is counted.

Which board partner card should you actually buy?

Among 12 GB RTX 3060 variants the differences are clocks, cooler size, and noise — not capability. The memory configuration that matters is fixed at 12 GB on a 192-bit bus across all of them.

The ZOTAC Gaming GeForce RTX 3060 Twin Edge 12GB is the compact two-fan option: shortest of the common variants, easiest fit in a mid-tower or a small-form-factor case, and the one to pick when the card is going into a box that will sit on a desk. It runs louder under sustained load than a three-fan design, which matters if the machine holds a model resident overnight.

The MSI GeForce RTX 3060 Ventus 2X 12G is the alternate at the same tier — also two-fan, also 12 GB, and typically the easier one to find in stock. Between the two, buy whichever is cheaper on the day; there is no inference-relevant difference.

One trap is worth naming explicitly. NVIDIA also shipped an 8 GB RTX 3060 on a 128-bit bus. It carries the same model name, ranks high in search results, and it cannot hold Phi-4 14B at four-bit. Before you buy anything, read the listing title for "12G" or "12GB" and confirm the 192-bit bus. The SpecPicks RTX 3060 benchmark page tracks the 12 GB configuration.

What else the build needs

The card is the decision; the host is the thing that stops the card being the bottleneck.

CPU. Offloaded layers and prompt tokenisation run on the host, so a slow CPU shows up as prefill latency. The AMD Ryzen 7 5800X is the comfortable AM4 choice — eight cores and sixteen threads at a 105 W TDP with a 4.7 GHz boost, and PassMark scores it at 27,679 CPU Mark with a 3,448 single-thread rating. Full specifications are on AMD's Ryzen 7 5800X product page.

Cheaper CPU. The AMD Ryzen 5 5600G trades four threads for an integrated Radeon GPU, and that iGPU is the real feature here: it drives your displays so the discrete card's 12 GB stays entirely with the model instead of losing several hundred megabytes to the desktop. PassMark rates it at 19,654 CPU Mark with a 3,178 single-thread score, and Notebookcheck records 10,551 points in Cinebench R23 multi-core. For an inference box, freeing VRAM beats two extra cores.

Storage. Model files are large and read once per load. The Crucial BX500 1TB SATA SSD holds a useful library of four-bit quants alongside a game install or two. Drive speed changes how long a model takes to reach VRAM the first time and nothing about generation throughput afterwards, so this is the component to buy cheap and spend the savings on VRAM.

Memory. Thirty-two gigabytes in dual channel is the sensible 2026 target. Sixteen works while everything fits in VRAM, and stops working the moment a layer spills.

Performance per dollar and per watt

Using launch MSRPs from TechPowerUp — $329 for the RTX 3060 12GB, $479 for the RX 6700 XT — and the matched 14B Q4_K_M LocalScore generation figures:

The tok/s-per-watt and tok/s-per-dollar rows are derived from the cited generation, power and MSRP figures rather than measured directly. Every input is sourced above.

Street prices are the caveat. The SpecPicks catalog tracked the ZOTAC Twin Edge OC 12GB at $499.99 on 2026-09-10, well above MSRP, because new stock of a 2021 card is scarce and priced accordingly. The sub-$300 tier this question implies is the used and open-box market, where both cards trade and where prices move week to week. Check the live listing before treating any of these ratios as a purchase price.

Verdict matrix

Get the RTX 3060 12GB if… you want a Phi-4 14B assistant running tonight, you paste documents into it, you care about noise and power on an always-on box, or you would rather not audit ROCm release notes before a driver upgrade. The measured 24.6 tok/s from llmrun.dev is a comfortable reading speed, and the 2.4× prefill advantage is the part you will actually feel.

Get the RX 6700 XT if… the machine is primarily a gaming PC and inference is occasional. It is genuinely the faster gaming card at this tier, it holds the same 14B models, and 15.7 tok/s is usable if you are not watching it. Buy it for the frames and accept the inference as a bonus.

Skip both and wait if… your target is a 27B or 32B model, long-context retrieval over your own documents, or serving more than one user. Those workloads exhaust 12 GB before they exhaust compute, and no quantization recovers the headroom. Step up to 16 GB or 24 GB instead — the SpecPicks 24 GB GPU guide covers that tier.

Bottom line

The RTX 3060 12GB is the pick, and the margin is larger than the spec sheet suggests. Phi-4 14B at Q4_K_M runs at 24.6 tok/s in 9.5 GB per llmrun.dev, prompt processing is roughly 2.4× the Radeon's on matched 14B tests, and the software setup is a driver install rather than a project.

The counter-case is real and worth stating. The RX 6700 XT is the better gaming card, it has more memory bandwidth, and on an identical llama.cpp code path it generates tokens slightly faster. If this machine games five nights a week and answers questions on the sixth, that ordering may well be the right one for you. Just do not buy it expecting the inference numbers to follow the gaming numbers, because they do not.

Related guides

• GPT-OSS 20B vs Phi-4 14B on 12 GB of VRAM — the model-side version of this decision
• RTX 3060 12GB local LLM quantization guide — which quant to pick and why
• Best GPU for Ollama under $300 with 12 GB VRAM — the wider field at this price
• RX 9070 XT vs RTX 3060 12GB for local LLM inference — what a current-gen Radeon changes
• Gaming while running a local LLM on 12 GB — VRAM contention in practice
• RTX 3060 benchmark data and RX 6700 XT benchmark data

Citations and sources

• TechPowerUp — GeForce RTX 3060 12 GB specifications (accessed 2026-09-10)
• TechPowerUp — Radeon RX 6700 XT specifications (accessed 2026-09-10)
• llmrun.dev — RTX 3060 12GB model benchmarks (accessed 2026-09-10)
• LocalScore — RTX 3060 12GB accelerator results (accessed 2026-09-10)
• LocalScore — Radeon RX 6700 XT accelerator results (accessed 2026-09-10)
• llama.cpp GitHub Discussion #10879 — cross-vendor GPU benchmark thread (accessed 2026-09-10)
• Microsoft — Phi-4 model card on Hugging Face (accessed 2026-09-10)
• AMD — ROCm system requirements and supported GPUs (accessed 2026-09-10)
• Hardware Corner — RTX 3060 12GB LLM benchmarks (accessed 2026-09-10)
• Hardware Corner — GPU ranking for local LLMs at 16K context (accessed 2026-09-10)
• TYO Lab — 64GB RAM, 12GB VRAM: the honest local LLM benchmark (accessed 2026-09-10)
• singhajit.com — LLM inference speed comparison (accessed 2026-09-10)
• llama.cpp — build documentation (Vulkan and HIP backends) (accessed 2026-09-10)
• vLLM — GPU installation guide (accessed 2026-09-10)
• KitGuru — AMD RX 6700 XT review (accessed 2026-09-10)
• Gamersleaguepros — RTX 3060 gaming performance guide 2026 (accessed 2026-09-10)
• PassMark — AMD Ryzen 7 5800X CPU Mark (accessed 2026-09-10)
• PassMark — AMD Ryzen 5 5600G CPU Mark (accessed 2026-09-10)
• Notebookcheck — AMD Ryzen 5 5600G benchmarks and specs (accessed 2026-09-10)
• AMD — Ryzen 7 5800X product page (accessed 2026-09-10)

This piece is editorial synthesis based on publicly available information. No independent first-party benchmarking is reported.

Live price comparison

Both cards trade on the used and open-box market, and prices move weekly. The side-by-side spec and price view is at RTX 3060 12GB vs RX 6700 XT.

For the NVIDIA side, the two variants worth pricing are the ZOTAC Gaming GeForce RTX 3060 Twin Edge 12GB and the MSI GeForce RTX 3060 Ventus 2X 12G. For the host, the AMD Ryzen 7 5800X, the AMD Ryzen 5 5600G, and the Crucial BX500 1TB SATA SSD are the parts referenced above.

Prices shown were last tracked at crawl time and may vary — check the listing for the current price. As an Amazon Associate, SpecPicks earns from qualifying purchases.