Default GPU flavor -> rtx-pro-6000 (Blackwell)

app.py

@@ -24,8 +24,8 @@ FLAVOR_GUIDE = """**Which GPU?** You're billed per-minute of actual runtime.
 | Flavor | VRAM | $/h | Best for |
 |---|---|---|---|
 | `l40sx1` | 48 GB | $1.80 | cheapest — pair with the **Low-VRAM** profile (int8) |
-| `a100-large` | 80 GB | $2.50 | **recommended** — fits the 22B model in bf16 |
-| `rtx-pro-6000` | 96 GB | $2.75 | a bit more headroom (Blackwell) |
+| `a100-large` | 80 GB | $2.50 | fits the 22B model in bf16 |
+| `rtx-pro-6000` | 96 GB | $2.75 | **recommended (default)** — Blackwell, more headroom |
 | `h200` | 141 GB | $5.00 | fastest — best for higher resolution / longer clips |
 """
 MAX_LOG = 60_000
@@ -163,7 +163,7 @@ PROFILE_LOWVRAM = "Low-VRAM (≤32 GB)"
 PROFILE_CUSTOM = "Custom"
 PROFILES = {
     PROFILE_QUALITY: {"quantization": "none", "optimizer_type": "adamw", "te_8bit": False,
-                      "rank": 32, "alpha": 32, "flavor": "a100-large"},
+                      "rank": 32, "alpha": 32, "flavor": "rtx-pro-6000"},
     PROFILE_LOWVRAM: {"quantization": "int8-quanto", "optimizer_type": "adamw8bit", "te_8bit": True,
                       "rank": 16, "alpha": 16, "flavor": "l40sx1"},
 }
@@ -354,7 +354,7 @@ with gr.Blocks(title="LTX-2.3 LoRA Trainer") as demo:
             hub_id = gr.Textbox(label="Hub model id", placeholder="username/my-lora")
             with gr.Row():
                 flavor = gr.Dropdown(FLAVORS, value=jobs.DEFAULT_FLAVOR, label="GPU flavor",
-                                     info="Auto-set from your profile (Quality → a100-large · Low-VRAM → l40sx1); "
+                                     info="Auto-set from your profile (Quality → rtx-pro-6000 · Low-VRAM → l40sx1); "
                                           "change it freely. See the guide below.")
                 timeout = gr.Textbox(label="Timeout", value="6h",
                                      info="Max job runtime (e.g. 4h). First run spends ~minutes downloading the model.")

jobs.py

@@ -29,7 +29,7 @@ import yaml
 from huggingface_hub import HfApi
 
 SRC_BUCKET = os.environ.get("LTX_SRC_BUCKET", "ltx-community/ltx2-trainer-src-v2")
-DEFAULT_FLAVOR = "a100-large"  # the largest single-GPU flavor `hf jobs uv run` accepts (80GB)
+DEFAULT_FLAVOR = "rtx-pro-6000"  # default single-GPU flavor (Blackwell, 96GB; cu128 torch in v2)
 VIDEO_EXTS = {".mp4", ".mov", ".mkv", ".webm", ".avi", ".m4v"}
 
 # Deterministic on-Job paths (the Space bakes these into config.yaml).