How much VRAM does the NVIDIA RTX 5070 Ti have?

The NVIDIA RTX 5070 Ti has 16 GB of GDDR7 with 896 GB/s memory bandwidth.

What is the NVIDIA RTX 5070 Ti best for?

With 16 GB of VRAM, the NVIDIA RTX 5070 Ti handles smaller models (7B–14B) at Q4–Q5 quantization, ideal for entry-level local LLM experimentation and lightweight inference.

What LLMs can the NVIDIA RTX 5070 Ti run locally?

The NVIDIA RTX 5070 Ti can run 46 of the 99 open-weight models tracked by CanItRun natively in VRAM at 8k context. Top options include: Qwen 3.8 27B at Q3_K_M, Ornith 1.5 35B-A3B (MoE) at Q2_K, Ornith 1.5 9B at NVFP4.

Can the NVIDIA RTX 5070 Ti run Gemma 4 31B?

Yes. The NVIDIA RTX 5070 Ti runs Gemma 4 31B natively in VRAM at Q2_K quantization, achieving approximately 44.1 tokens per second.

Can the NVIDIA RTX 5070 Ti run Qwen 3.6 27B?

Yes. The NVIDIA RTX 5070 Ti runs Qwen 3.6 27B natively in VRAM at Q3_K_M quantization, achieving approximately 43.1 tokens per second.

Can the NVIDIA RTX 5070 Ti run Qwen3 8B?

Yes. The NVIDIA RTX 5070 Ti runs Qwen3 8B natively in VRAM at NVFP4 quantization, achieving approximately 111.8 tokens per second.

NVIDIA RTX 5070 Ti

The NVIDIA RTX 5070 Ti has 16 GB VRAM and 896 GB/s memory bandwidth. It can run 46 of our 99 tracked models natively in VRAM at 8k context.

With 16 GB GDDR7, the NVIDIA RTX 5070 Ti is a consumer-tier GPU that can run 46 models natively. This site's calculator runs Qwen3 14B at its recommended Q5_K_M quantization (13.31 GB total) at 49.0 tok/s, and GPT-OSS 20B, a model OpenAI explicitly sized to fit a 16GB card, at its recommended Q4_K_M (14.55 GB total) at 77.5 tok/s, both with several GB of the 16GB ceiling still free for context. Llama 3.1 8B fits at its recommended Q5_K_M with room to spare (7.58 GB, 86 tok/s). Independent benchmarks from hardware-corner.net (whose RTX 4090 numbers already calibrate this site's own decode-efficiency model) report the same bandwidth-bound falloff at longer context: Qwen3 14B at Q4_K holds 58.0 tok/s at 16k context, down from 74.3 tok/s at 4k, and Qwen3 8B at Q4_K holds 87.5 tok/s at 16k, down from 120.5 tok/s at 4k, community-reported, not this site's own figures, but the same shape this site's KV-cache-aware estimate produces. 32B-class models don't fit natively at any quantization: Qwen3 32B needs 19.87 GB even at the newest NVFP4 format, past this card's ~15.2 GB of usable VRAM, so CPU offload is required for anything past the 20B-class ceiling.

The NVIDIA RTX 5070 Ti launched February 20, 2025 at a $749 MSRP, sharing the RTX 5080's 16GB GDDR7 capacity on a cut-down GB203 die. With 8,960 CUDA cores and 896 GB/s of bandwidth, it runs 7B–14B LLMs entirely in VRAM and holds up to a 20B-class MoE model like GPT-OSS 20B.

NVIDIA RTX 5070 Ti: Launched February 20, 2025 at a $749 MSRP, NVIDIA's official price, though GamersNexus's launch review ("Do Not Buy: NVIDIA RTX 5070 Ti GPU Absurdity") found board-partner cards already listed from $850-$900 at launch, with at least one model spotted near $1,000. Built on a cut-down GB203-300 die (92.2 billion transistors), the same GB203 silicon as the RTX 5080, which gets the fuller GB203-400 variant with 10,752 CUDA cores versus this card's 8,960 (16.7% fewer). Shares the RTX 5080's exact 16GB GDDR7 capacity and 256-bit bus, at 896 GB/s versus the 5080's 960 GB/s (28 vs 30 Gbps per pin), a 6.7% bandwidth gap for a $250 lower launch price. The same GamersNexus review measured it landing within a few percent of the RTX 4080 Super in gaming and 9-16% behind the RTX 5080.

This site's calculator runs Qwen3 14B at its recommended Q5_K_M quantization (13.31 GB total) at 49.0 tok/s, and GPT-OSS 20B, a model OpenAI explicitly sized to fit a 16GB card, at its recommended Q4_K_M (14.55 GB total) at 77.5 tok/s, both with several GB of the 16GB ceiling still free for context. Llama 3.1 8B fits at its recommended Q5_K_M with room to spare (7.58 GB, 86 tok/s). Independent benchmarks from hardware-corner.net (whose RTX 4090 numbers already calibrate this site's own decode-efficiency model) report the same bandwidth-bound falloff at longer context: Qwen3 14B at Q4_K holds 58.0 tok/s at 16k context, down from 74.3 tok/s at 4k, and Qwen3 8B at Q4_K holds 87.5 tok/s at 16k, down from 120.5 tok/s at 4k, community-reported, not this site's own figures, but the same shape this site's KV-cache-aware estimate produces. 32B-class models don't fit natively at any quantization: Qwen3 32B needs 19.87 GB even at the newest NVFP4 format, past this card's ~15.2 GB of usable VRAM, so CPU offload is required for anything past the 20B-class ceiling.

Same Blackwell compute capability (sm_120) as every other desktop 50-series card, so it inherits the same early-driver rough edges: NVIDIA's own engineering blog measured a ~27% LM Studio/llama.cpp speedup on the sibling RTX 5080 just from upgrading to the CUDA 12.8 runtime Blackwell requires, and a very old llama.cpp, Ollama, or PyTorch build may still not recognize this GPU's architecture. This card can also run NVFP4, a 4-bit format newer than the legacy GGUF K-quants and the one this site's calculator picks as the best fit for both Qwen3 14B (9.79 GB, 66.6 tok/s) and GPT-OSS 20B (13.11 GB, 93.9 tok/s), but it needs CUDA and isn't yet as widely packaged for Ollama/llama.cpp as Q4_K_M/Q5_K_M/Q6_K are. On value: this is not the cheapest way to 16GB in the Blackwell lineup; the RTX 5060 Ti 16GB's NVIDIA-announced $429 MSRP undercuts it by $320 for the identical capacity, trading half the bandwidth (448 vs 896 GB/s) to get there. What this card actually buys is speed at that shared 16GB ceiling over the 5060 Ti, and over the 5080, a $250 discount for a 6.7% bandwidth cut, a real but modest gap, not the "best price-to-VRAM ratio in the lineup" some launch coverage claimed.

Vendor	NVIDIA
Architecture	Blackwell
VRAM	16 GB
Memory type	GDDR7
Memory bandwidth	896 GB/s
Compute backend	CUDA
Tier	Consumer
Released	2025
Models (native)	46 / 99
Models (offload)	12 / 99

Software: Full llama.cpp and Ollama support out of the box. CUDA 12.x recommended; driver ≥ 525 required.

Same 16GB as the RTX 5080, on 6.7% less bandwidth

Plotting every desktop Blackwell GPU this site tracks by VRAM and bandwidth together shows something the RTX 5060 page's version of this same chart doesn't: three completely different cards landing on the exact same 16GB ceiling, at three different speeds.

VRAM and memory bandwidth, from each card's real spec sheet. A card further right holds bigger models; a card further up decodes them faster once they fit.

Three cards in this stack land on exactly 16GB (the RTX 5060 Ti 16GB, this card, and the RTX 5080) but at three different bandwidths: 448, 896, and 960 GB/s. This card and the RTX 5080 are the closer pair: identical 16GB capacity, and only 64 GB/s (6.7%) separates their bandwidth, since both share the same GB203 die and 256-bit bus, just with different core counts and clocks enabled. That's a much smaller gap than either card has to the RTX 5060 Ti 16GB two tiers down, which matches this card's VRAM exactly but trails its bandwidth by exactly half (448 vs 896 GB/s). Below 16GB, VRAM and bandwidth climb together in lockstep: RTX 5050 (8GB, 320 GB/s) to RTX 5060 (8GB, 448 GB/s) to RTX 5070 (12GB, 672 GB/s); this is the point in the stack where capacity plateaus at 16GB across three different cards while bandwidth keeps climbing, all the way to the RTX 5090's 32GB at 1,792 GB/s.

This generation grew both VRAM and bandwidth: most Blackwell steps only move one

Most generational refreshes in this stack only move one of these two numbers. The step from the direct predecessor, the RTX 4070 Ti, to this card moved both at once:

NVIDIA RTX 4070 Ti

504 GB/s

NVIDIA RTX 5070 Ti (this page)

896 GB/s

The RTX 4070 Ti to RTX 5070 Ti step grew VRAM 33.3% (12GB to 16GB) and bandwidth 77.8% (504 to 896 GB/s) in the same generational jump, GDDR6X swapped for faster GDDR7 on top of more capacity, not just one or the other. That's a sharper bandwidth gain than the RTX 5060 saw over the RTX 4060 (64.7%, with zero VRAM movement), and unlike the 5060, this card's capacity grew too. Worth knowing: NVIDIA already took part of this step mid-generation, before Blackwell existed. The RTX 4070 Ti SUPER refresh matched this card's 16GB capacity and reached 672 GB/s on January 24, 2024, a full year earlier, at a $799 MSRP. Measured against that refresh instead of the original RTX 4070 Ti, the real jump to this card is a smaller 33.3% bandwidth gain (672 to 896 GB/s) with no further VRAM growth at all, a very different generational story depending on which "previous generation" card is the baseline.

What that 6.7% bandwidth gap is worth in real tokens per second

Same 16GB capacity doesn't mean the same speed. Running one real model at one real quantization on both cards shows exactly what the RTX 5080's small bandwidth edge buys once actual weights are decoding:

NVIDIA RTX 5070 Ti (this page)

49 tok/s

NVIDIA RTX 5080

52.5 tok/s

Qwen3 14B at its recommended Q5_K_M quantization (13.31 GB total: weights, KV cache, and overhead, comfortably inside both cards' 16GB) decodes at 49 tok/s on this card and 52.5 tok/s on the RTX 5080. That 6.7% gap matches the two cards' 896 vs 960 GB/s bandwidth difference almost exactly, because at identical capacity and identical weights, decode speed on this site's calculator is essentially a direct function of bandwidth. Put another way: the RTX 5080 doesn't run anything this card can't; every model that fits one fits the other, at the identical quantization; it just runs it 6.7% faster, for $250 more at NVIDIA's official launch pricing ($749 vs $999).

Popular models for this GPU

Qwen 3.5 35B-A3B (MoE)Ornith 1.5 35B-A3B (MoE)Nemotron 3 Nano 30B Gemma 4 31B Qwen3 30B-A3B (MoE)

Models this GPU runs natively in VRAM (46)

Show 41 more

Models that fit with CPU offload (12)

These use system RAM for layers that don't fit in VRAM, so expect much slower inference.

Too large for this GPU (41)

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NVIDIA RTX 50 Series (Blackwell) for LLMs: Complete Guide

Frequently asked questions

How much VRAM does the NVIDIA RTX 5070 Ti have?: The NVIDIA RTX 5070 Ti has 16 GB of GDDR7 with 896 GB/s memory bandwidth.
What is the NVIDIA RTX 5070 Ti best for?: With 16 GB of VRAM, the NVIDIA RTX 5070 Ti handles smaller models (7B–14B) at Q4–Q5 quantization, ideal for entry-level local LLM experimentation and lightweight inference.
What LLMs can the NVIDIA RTX 5070 Ti run locally?: The NVIDIA RTX 5070 Ti can run 46 of the 99 open-weight models tracked by CanItRun natively in VRAM at 8k context. Top options include: Qwen 3.8 27B at Q3_K_M, Ornith 1.5 35B-A3B (MoE) at Q2_K, Ornith 1.5 9B at NVFP4.
Can the NVIDIA RTX 5070 Ti run Gemma 4 31B?: Yes. The NVIDIA RTX 5070 Ti runs Gemma 4 31B natively in VRAM at Q2_K quantization, achieving approximately 44.1 tokens per second.
Can the NVIDIA RTX 5070 Ti run Qwen 3.6 27B?: Yes. The NVIDIA RTX 5070 Ti runs Qwen 3.6 27B natively in VRAM at Q3_K_M quantization, achieving approximately 43.1 tokens per second.
Can the NVIDIA RTX 5070 Ti run Qwen3 8B?: Yes. The NVIDIA RTX 5070 Ti runs Qwen3 8B natively in VRAM at NVFP4 quantization, achieving approximately 111.8 tokens per second.